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      "instructors": ["Bireswar Das", "Balagopal Komarath", "Anirban Dasgupta"],
      "content": "Finite Automata and Regular Languages: Deterministic and Nondeterministicfinite automata and their equivalence, Pumping lemma, the Myhill-Nerode Theorem; PushdownAutomata (PDA) and Context free languages (CFL): Context free grammars (CFG), normal forms,variants of pushdown automata, equivalence of PDA and CFG, pumping lemma for CFLs; TuringMachine (TM) and Computability: Variants of TM and their equivalence, Decidability andUndecidability, Halting problem, reductions, Rice's theorem; Complexity Theory: Time Complexity, P,NP, many-one reductions, NP-completeness, Cook-Levin theorem, Space Complexity.",
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      "content": "Version controlling local, centralized, distributed; Agile development methods: Continuous integration/delivery/deployment. e.g., Git workflow, actions etc.; Makefiles and build tools: Dependency rules, macros, suffix rules etc. build tools e.g., Gradle, Apache Maven etc.; Rapid application development: introduction to event-driven programing, windows form controls and event handling; Data and connectivity: Data binding in windows forms, connecting controls to data sources, displaying, and updating data; Debugging/profiling: Visual studio debugger, GNU debugger, Linux perf - stat, trace, sampling and off-CPU profiling, hotspot UI frontend.",
      "textbooks": "1. Sharp, J. (2018). Microsoft Visual C# Step by Step. Microsoft Press. 2. Watson, K., Nagel, C., Pedersen, J. H., Reid, J. D., & Skinner, M. (2010). Beginning Visual C# 2010. John Wiley & Sons. 3. Szostak, T. (2013). Windows Phone 8 Application Development Essentials (A practical guide to creating a Windows Phone 8 application using C#, XAML, and MVVM). Packt. References: 4. Virtual Machine for Windows development environment. [Link] 5. Soni, M. (2016). DevOps for Web Development. Packt Publishing Ltd.",
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      "content": "Data collection and labeling: collecting data: tools for logging/instrumenting sources (e.g. website) to collect usage data; validating data; labeling data: tools such as label studio to annotate datasets, methods such as Cohen Kappa inter-annotator agreement, snowball sampling, active learning for cost-effective data annotation; data augmentation; model and code reproducibility and versioning: architecture and hyperparameter search: tools such as weights and biases for architecture and hyperparameter search and various AutoML tools such as AutoGluon; model checkpointing: tools such as MLFlow, Tensorboard/weights and biases for model/weights checkpointing; data versioning; self-contained environments: tools such as Docker for managing released packages; cloud platforms such as GCP/AWS/Azure; Continuous Integration/Co: A/B tests; distribution, covariate shift; redeployment; deployment on constrained devices: tools such as TF-lite, topics like quantization, etc.",
      "textbooks": "1. Murphy, K.P., 2022. Probabilistic machine learning: Advanced topics. MIT press. 2. Murphy, K.P., 2022. Probabilistic machine learning: an introduction. MIT press 3. Prince, S.J., 2023. Understanding deep learning. MIT press. 4. Gift, Noah, and Alfredo Deza. Practical MLOps. O'Reilly Media, Inc., 2021. 5. Trustworthy online controlled experiments: A practical guide to A/B testing by Ron Kohavi, Diane Tang, Ya Xu 6. Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc., 2022. References: 7. CS 329 course from Stanford University https://stanford-cs329s.github.io/syllabus.html",
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      "content": "Finite Automata and Regular Languages: Deterministic and Nondeterministic finite automata and their equivalence, Pumping lemma, the Myhill-Nerode Theorem.Pushdown Automata (PDA) and Context free languages (CFL): Context free grammars (CFG), normal forms, variants of pushdown automata, equivalence of PDA and CFG, pumping lemma for CFLS.Turing Machine (TM) and Computability: Variants of TM and their equivalence, Decidability and Undecidability, Halting problem, reductions, Rice's theorem.Complexity Theory: Time Complexity, P, NP, many-one reductions, NP-completeness, Cook- Levin theorem, Space Complexity.",
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        "Anirban Dasgupta",
        "Mayank Singh",
        "Shan"
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      "content": "1. Refresher Module; Data representation (floating point arithmetic, underflow, overflow); Linear Algebra; Calculus (univariate calculus); basic probability2. Multivariate Calculus and Automatic Differentiation; Gradient, Jacobian, Hessian; Finite difference methods and limitations; Automatic gradient computation; Continuous Multivariate Probability distributions; Maximum likelihood estimation (MLE) and Maximum A Posteriori (MAP); Conjugate Priors3. Optimization; Continuous optimization:i. Taylor's series and connections to optimization methodsii. Unconstrained Optimization:1. Gradient descent.2. Gauss-Newton methods and variants.iii. Constrained Optimization: Lagrange multipliers, primal and dual forms, KKTconditions.iv. Linear and Quadratic Programming4. Unification of Information Theory and Machine Learning; Information Theoretic concepts: self-information, entropy, KL divergence; Perfect communication over a noisy imperfect communication channel: Datacompression problem: Optimal code, Huffman encoding5. Markov chains :sequential modeling, joint probability of a sequence, representation of achain, learning the parameters given a sequence.",
      "textbooks": "Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics formachine learning. Cambridge University Press, 2020.; Solomon, J. (2015). Numerical algorithms: methods for computer vision, machinelearning, and graphics. CRC press.; Boyd, Stephen P., and Lieven Vandenberghe. Convex optimization. Cambridgeuniversity press, 2004.; MacKay, David JC. Information theory, inference and learning algorithms.Cambridge university press, 2003.; James, G., Witten, D., Hastie, T., Tibshirani, R. and Taylor, J., 2023. Statisticallearning. In An Introduction to Statistical Learning: with Applications in Python (pp.15-67). Cham: Springer International Publishing.; Allen, B. D. (2013). Think Bayes: Bayesian Statistics in Python.; Downey, A. B. (2011). Think stats. O'Reilly Media, Inc..; References:; Fernandez-Granda, C. (2017). Probability and Statistics for Data Science.; Rabiner, L. R. (1989). A tutorial on hidden Markov models and selectedapplications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.; Baydin, A. G., Pearlmutter, B. A., Radul, A. A., & Siskind, J. M. (2018). Automaticdifferentiation in machine learning: a survey. Journal of Machine LearningResearch, 18, 1-43.; Goh, G. (2017). Why momentum really works. Distill, 2(4), e6.; Peyré, G. (2020). Course notes on optimization for machine learning.; Peyré, G. (2020). Mathematical foundations of data sciences. Rn, 1, 2.",
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      "discipline": "Comp Science & Engg",
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      "courseType": "Others",
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      "semesters": ["Full Semester"],
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      "instructors": [
        "Neeldhara",
        "Nipun",
        "Shanmuga",
        "Mayank",
        "Anirban",
        "Uttam"
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      "content": "Introduction to AI. Flavors of AI: strong and weak, neat and scruffy, symbolicand subsymbolic, knowledge-based and data-driven. The computational metaphor. What iscomputation? Church-Turing thesis. The Turing test. Searle's Chinese room argument; Representing Knowledge. Production rules, monotonic and non-monotonic logics, semanticnets, frames and scripts, description logics; Algorithmic, game-theoretic and logical foundationsof multi-agent systems. Distributed optimization and problem solving, non-cooperative gametheory, learning and teaching, communication, social choice, mechanism design, auctions,negotiation, coalitional game theory, logics of knowledge and belief, collaborative plans andsocial systems; Fairness, Ethics, and Bias. Algorithmic fairness: Individual fairness, Groupfairness. Applications of fairness in AI: Fairness in natural language processing, Fairness incomputer vision, Deepfakes, Federated learning",
      "textbooks": "Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach, 4th Global ed.; Moulin, H. (2016). Handbook of Computational Social Choice (F. Brandt, V. Conitzer, U.Endriss, J. Lang, & A. Procaccia, Eds.). Cambridge: Cambridge University Press.doi:10.1017/CBO9781107446984References:Selected papers from the ACM conference on Fairness, Accountability, and Transparency",
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      "code": "CS 330",
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      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Sameer Kulkarni", "Abhishek Bichhawat"],
      "content": "Operating systems basics: Evolution, history and design principles; Processes and threads: Kernel/user level operation, thread/process synchronization, system calls and APIs, interrupt handling, CPU scheduling; Memory management: virtual memory, address spaces and translations, segmentation and paging, TLBs and other caching techniques; Concurrency and synchronization: Race conditions, locks, semaphores, concurrent data structures; I/O systems and storage devices: Hard disk drives, solid state devices, file systems design principles and implementation, maintaining data integrity; Advanced topics: Virtualization, distributed systems.",
      "textbooks": "1. Arpaci-Dusseau, Remzi H. and Arpaci-Dusseau, Andrea C. Operating Systems - Three Easy Pieces, (available online) 2. Anderson, Thomas and Dahlin, Michael, Operating Systems: Principles and Practices, 2nd edition 3. Silberschatz, Abraham and Galvin, Peter B. and Gagne, Greg, Operating Systems Concepts, 10th edition",
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      "code": "CS 331",
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      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
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      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Sameer Kulkarni"],
      "content": "Introduction to Computer Networks: Layered Network Protocol Architectures: OSI and TCP/IP reference models. Overview of Telecommunications, Cellular networks & Internet Architecture; Physical and Link Layers: Basics of communications; Delay, Loss and Throughput. Physical media types and their important bandwidth and bit-error-rate characteristics; Wired and Wireless media including copper cables, optical fiber and wireless. Framing; Error control: Bit-parity, CRC and Hamming Codes; Medium Access Control (MAC): Shared media systems; Bus, Star and Ring topologies; TDMA, FDMA, CSMA, CSMA/CD, Ethernet and IEEE 802.3; IEEE 802.11 including CSMA/CA protocols; Shared and Switched Ethernet; Introduce protocols such as ARP, RARP and ICMP; Network Layer: Design Principles: Protocols, Addressing and Routing. Study of Internet Protocol (IP) suite; Hierarchical network architectures; IPv4 and IPv6 addressing and headers; Routing protocols: distance-vector and link-state approaches; Interior and Exterior Gateway Protocol concepts; Routing Algorithms including Dijkstra's algorithm and distributed Bellman-Ford algorithm; Example protocols: OSPF, RIP, BGP; Transport Layer: Connection oriented and Connectionless protocols: TCP and UDP; Details of TCP: Buffer Management, Flow Control and Congestion control; Study of TCP variants such as Reno, Tahoe, Vegas and CUBIC. Reliable transmission and Automatic Repeat Request (ARQ) protocols: Stop-and-Wait, Go-back-N, Selective Repeat; Application Layer: Study of Protocols and Applications: DNS, FTP, HTTP, and SMTP. Socket Interface and Socket programming; Advanced topics and recent trends in Computer Networks and Future Internet: Information Centric networking, Software Defined Networking (SDN), Network Function Virtualization (NFV), Internet of Things (IoT), Docker and Container Networking (CNIs), Programmable switches, OVS, and SmartNICs; Miscellaneous: Introduction to Datacenter Networks, Different RFCs; Case Studies: Linux/Unix TCP IP Stack; Network Simulators: NS-3, and Mininet; Optional: NS-2, OMNet,",
      "textbooks": "1. James Kuross and Keith Ross, Computer Networking A Top Down Approach 7th Edition, Pearson Publishers, 2017. (Recommended) 2. Andrew S. Tanenbaum and David J. Wetherall, Computer Networks, 5th Edition, Pearson Publishers Recursive Books, 2014. Additional Reference Books: 3. Larry L. Peterson, and Bruce S.Davie, Computer Networks: A Systems Approach, 5th Edition, Morgan Kaufmann, 2012. (Reference) 4. Richard Stevens, UNIX Network Programming 2nd Edition, Pearson Publishers, 2015 (Tutorials and Hands on Programming Exercises) Other Online References: 5. TCP/IP Technical Overview, Tutorial and Guide, a. Charles M. Kozierok, Available Online - http://www.tcpipguide.com/",
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    },
    {
      "id": "cs-333-ethics-of-ai",
      "code": "CS 333",
      "title": "Ethics of AI",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "300 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [
        "P. Manisha",
        "Neeldhara Misra",
        "Nipun Batra",
        "Anirban"
      ],
      "content": "Introduction to AI Ethics and Accountabilityo Discuss the critical role ethics play in AI research and deployment, emphasizing the potential societal impactsand the necessity for responsible innovation to prevent harm and promote trust.o Identify stakeholders responsible for AI outcomes, including developers, organizations, and policymakers,and discuss the allocation of accountability.Algorithmic Bias and Fairnesso Introduction: Case studies on biased AI systems and explore sources of bias.o Fairness Definitions: Sufficiency, Separation and Calibration and their relationship with each other andaccuracyo Fairness Approaches: Explore one in-processing, pre-processing and post-processing approach withapplication to real-world dataset for mitigating biasPrivacyo Case studies on Data Privacyo Reconstruction and Membership Inference Attackso Introduction to Differential Privacyo Differentially Private - Stochastic Gradient DescentInterpretabilityo Motivation and Goals for Interpretabilityo Overview of Model Agnostic Methodso Overview of Model Specific Methods.o Neural network interpretationGlobal Perspectives and Regulationso International policies and guidelines on AI ethics.o Comparative analysis of AI regulations across countries.o The role of organizations in setting ethical standards.",
      "textbooks": "1. Barocas, Solon, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning, 2018.2. Molnar, Christoph. Interpretable machine learning, 2019.3. https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdfReferences and Related Courses:4. https://ethics-of-ai.mooc.fi/start5. http://www.gautamkamath.com/CS860-fa2020.html6. https://hci.stanford.edu/courses/cs335/2020/sp/schedule.html7. https://www.cs.cornell.edu/courses/cs4732/2017sp/8. http://maartensap.com/11-830-Spring2023/9. https://ai4sibook.org/",
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      "semester": "Full Semester",
      "semesters": ["Full Semester"],
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      "instructors": ["Bireswar Das"],
      "content": "Special Topics",
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      "code": "CS 391-I",
      "title": "Special Topics in Computer Science: Virtual & Augmented Reality Systems",
      "credits": [2],
      "creditLabel": "2",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "300 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Half Semester",
      "semesters": ["Half Semester"],
      "prerequisites": [],
      "instructors": ["Prof Samit Bhattacharya"],
      "content": "The course intends to build foundation in the area of Extended Reality, covering virtual, augmented and mixed reality. The topics to be covered include introduction to the field, the hardware and software used to build and deploy such systems, understanding and implementing the immersive experience.",
      "textbooks": "1. LaValle, S. M. (2023). Virtual Reality. Cambridge University Press. (available online)2. Gerald, J. (2015). The VR Book: Human-Centered Design for Virtual Reality. ACM and & Morgan & Claypool Publishers.3. Bhattacharya, S. (2019). Human-Computer Interaction: User-Centric Computing for Design. McGraw-Hill India.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-392-i-special-topics-in-computer-science-introduction-to-competitive-programming",
      "code": "CS 392-I",
      "title": "Special Topics in Computer Science: Introduction to Competitive Programming",
      "credits": [2],
      "creditLabel": "2",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "300 level",
      "lecture": 3,
      "theory": 0,
      "practical": 3,
      "ltpLabel": "3-0-3",
      "semester": "Half Semester",
      "semesters": ["Half Semester"],
      "prerequisites": [],
      "instructors": ["All CSE and CSE adjacent faculty members"],
      "content": "This is a course focused on teaching algorithms and data structures through problem solving. Students will be solving andcoding up solutions to various basic algorithmic and data structure related problems, from the lens of competitiveprogramming. Familiarizing with online judges: Understanding the various components of a problem statement, the importance of constraints, understanding hidden test files, verdicts, following the exact Input/Output prescribed in the statement, flowcharts, complexity analysis. Arrays: 1d and 2d array based problems, frequency arrays, inverse arrays, two-pointer methods, prefix sums, difference arrays, sliding windows. Sorting and Searching: Sorting algorithms, ad-hoc sorting problems, linear search, binary search, binary search on answer. Greedy: Introduction to greedy algorithms, proof techniques based on exchange arguments. Dynamic Programming (DP): Classical DP problems (0/1 Knapsack, Longest Increasing Subsequence, Edit Distance, Longest Common Subsequence, Subset Sum), Grid DPs, DP on intervals, Bitmask DP. Math for CP: Euclid's Greatest Common Divisor algorithm, Sieve of Eratosthenes, Modular arithmetic, primality testing, factorization.",
      "textbooks": "There is no single textbook. Problems will be from online platforms (eg. AtCoder, Codeforces, CodeChef).; Cormen, T. H., Leiserson, C. E., Rivest, R. L., &amp; Stein, C. (2022). Introduction to Algorithms (4th ed.). MIT Press.; Laaksonen, A. (2018). Competitive Programmer's Handbook.; Halim, S., Halim, F., &amp; Effendy, S. (2020). Competitive Programming 4: The Lower Bound of ProgrammingContests in the 2020s (4th ed.). Lulu Press.; Skiena, S. S. (2020). The Algorithm Design Manual (3rd ed.). Springer.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-421-information-security",
      "code": "CS 421",
      "title": "Information Security",
      "credits": [6],
      "creditLabel": "6",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 2,
      "theory": 0,
      "practical": 2,
      "ltpLabel": "2-0-2",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-422-data-mining",
      "code": "CS 422",
      "title": "Data Mining",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-424-sensors-phones-and-cloud-computation-applied-to-hazard-mitigation",
      "code": "CS 424",
      "title": "Sensors, Phones and Cloud Computation applied to Hazard Mitigation",
      "credits": [2],
      "creditLabel": "2",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-426-markov-chains-and-queueing-models",
      "code": "CS 426",
      "title": "Markov Chains and Queueing Models",
      "credits": [2],
      "creditLabel": "2",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-427-reliability-engineering",
      "code": "CS 427",
      "title": "Reliability Engineering",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-428-introduction-to-applied-cryptography",
      "code": "CS 428",
      "title": "Introduction to Applied Cryptography",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-429-introduction-to-computer-graphics",
      "code": "CS 429",
      "title": "Introduction to Computer Graphics",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-430-algorithms-for-data-science",
      "code": "CS 430",
      "title": "Algorithms for Data Science",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-431-computer-and-network-security",
      "code": "CS 431",
      "title": "Computer and Network Security",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-432-databases",
      "code": "CS 432",
      "title": "Databases",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": "Databases",
      "textbooks": "Databases",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-433-computer-networks",
      "code": "CS 433",
      "title": "Computer Networks",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Sameer Kulkarni", "Anirban Dasgupta"],
      "content": "Introduction to Computer Networks: Layered Network Protocol Architectures: OSI and TCP/IP reference models. Overview of Telecommunications, Cellular networks & Internet Architecture. Physical and Link Layers: Basics of communications; Delay, Loss and Throughput. Physical media types and their important bandwidth and bit-error-rate characteristics; Wired and Wireless media including copper cables, optical fiber and wireless. Framing; Error control: Bit-parity, CRC and Hamming Codes. Medium Access Control (MAC): Shared media systems; Bus, Star and Ring topologies; TDMA, FDMA, CSMA, CSMA/CD, Ethernet and IEEE 802.3; IEEE 802.11 including CSMA/CA protocols; Shared and Switched Ethernet; Introduce protocols such as ARP, RARP and ICMP.Network Layer: Design Principles: Protocols, Addressing and Routing. Study of Internet Protocol (IP) suite; Hierarchical network architectures; IPv4 and IPv6 addressing and headers; Routing protocols: distance-vector and link-state approaches; Interior and Exterior Gateway Protocol concepts; Routing Algorithms including Dijkstra's algorithm and distributed Bellman-Ford algorithm; Example protocols: OSPF, RIP, BGP.Transport Layer: Connection oriented and Connectionless protocols: TCP and UDP; Details of TCP: Buffer Management, Flow Control and Congestion control; Study of TCP variants such as Reno, Tahoe, Vegas and CUBIC. Reliable transmission and Automatic Repeat Request (ARQ) protocols: Stop-and-Wait, Go-back-N, Selective Repeat.Application Layer: Study of Protocols and Applications: DNS, FTP, HTTP, and SMTP. Socket Interface and Socket programming.Advanced topics and recent trends in Computer Networks and Future Internet: Information Centric networking, Software Defined Networking (SDN), Network Function Virtualization (NFV), Internet of Things (IoT), Docker and Container Networking (CNIs), Programmable switches, OVS, and SmartNICs.Tutorials will also include hands-on programming and problem solving. Miscellaneous: Introduction to Datacenter Networks, Different RFCs.Case Studies: Linux/Unix TCP IP Stack.Network Simulators: NS-3, and Mininet.Optional: NS-2, OMNet,",
      "textbooks": "Text Books:1. James Kuross and Keith Ross, Computer Networking A Top Down Approach 7th Edition, Pearson Publishers, 2017. (Recommended)2. Andrew S. Tanenbaum and David J. Wetherall, Computer Networks, 5th Edition, Pearson Publishers Recursive Books, 2014.Additional Reference Books:3. Larry L. Peterson, and Bruce S.Davie, Computer Networks: A Systems Approach, 5th Edition, Morgan Kaufmann, 2012. (Reference)4. Richard Stevens, UNIX Network Programming 2nd Edition, Pearson Publishers, 2015 (Tutorials and Hands on Programming Exercises)Other Online References:5. TCP/IP Technical Overview, Tutorial and Guide, a) Charles M. Kozierok, Available Online - http://www.tcpipguide.com/b) TCP/IP Tutorial and Technical Overview, (IBM Redbook) - Download From http://www.redbooks.ibm.com/abstracts/gg243376.html 6. Request for Comments (RFC) - IETF - http://www.ietf.org/rfc.html7. Research Publications - relevant works will be discussed and distributed time to time",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-434-software-engineering-and-testing",
      "code": "CS 434",
      "title": "Software Engineering and Testing",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [
        "Shouvick Mondal",
        "Sameer Gundurao Kulkarni",
        "Abhishek Bichhawat"
      ],
      "content": "Basics of software engineering: software versus product; why software engineering; developmentstyles; Software development life cycle: models; classic; waterfall; agile; scrum; devOps; Phases:requirement analysis; feasibility study; software design; software architecture; coding; testing; release; maintenance; Types of testing: unit; system; regression; white box; grey box; black box; Software maintenance: evolution; change analysis; test-case selection; prioritization; reduction; debugging; fault localization, version control; Software reverse engineering: decompilation; binarylifting; Advanced topics: differential testing; domain specific testing, flaky tests, test parallelization,test dependency detection.",
      "textbooks": "1. Rajib Mall. 2014. Fundamentals of Software Engineering (4th. edition). Prentice-Hall of IndiaPvt.Ltd.References:2. Paul Ammann and Jeff Offutt. 2008. Introduction to Software Testing (1st. edition). CambridgeUniversity Press, USA.3. Some selected papers from SE conferences: ICSE, FSE, ASE, ISSTA, ICST",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-435-human-computer-interaction",
      "code": "CS 435",
      "title": "Human-Computer Interaction",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Yogesh Kumar Meena"],
      "content": "Introduction: Human-Computer Interaction (HCI); the role of an effective design(example of good and bad design) in the interaction design process; Concepts:describes major design principles associated with HCI e.g., visibility, flow,affordance, metaphors, feedback, consistency etc.; Understanding users anddata gathering: Overview of how to plan and run successful data gatheringsessions including interviews, design questionnaires and run observations; Requirements formulation: describes how to interpret results from datagathering sessions into requirements for design, explains the process andpurpose of literature reviews and goes through how to develop scenarios anduse cases; User interfaces and modalities: introduction to the many differenttypes of user interface and modalities, and how they are used; Design,prototyping and construction: explain why to prototype and how to constructprototypes (low and high-fidelity), and how to begin construction based ondesigns; Evaluation: why is evaluation important, goals, methods, case study; Data analysis: discuss the qualitative and quantitative data and describes howbest to analyze the results from data gathering sessions; Group course project(low/high-fidelity prototype) submission and presentation; Future directions:what is next for HCI and interaction design?",
      "textbooks": "1. Human-computer interaction. Pearson Education. Dix, A., Finlay, J., Abowd,G. D., & Beale, R. 2003.2. Interaction design: beyond human-computer interaction, Jenny Preece,Yvonne Rogers and Helen Sharp. Chichester: John Wiley & Sons 20193. The design of everyday things, Donald A. Norman. Cambridge,Massachusetts: MIT Press 20134. There's not an app for that: mobile user experience design for life / SimonRobinson, Gary Marsden and Matt Jones. Amsterdam; Boston: ElsevierLogo 20145. Mobile interaction design, Matthew Jones. John Wiley 2005References:1. Cognitive processes: attention, perception, memory, thinking and language.Bloomsbury Publishing. Malim, T. 1994.2. Groome, D. (2014). An Introduction to Cognitive Psychology: Processesand Disorders. 3rd Ed. Psychology Press.3. Discovering statistics using R, Andy Field, Jeremy Miles and ZoeÌˆ Field.Sage 20124. Discovering statistics using IBM SPSS statistics Sage Publications 20185. Statistics without tears: an introduction for nonmathematicians, PenguinBooks 2018.6. Research methods for human-computer interaction, edited by Paul Cairnsand Anna L. Cox. Cambridge University Press 20087. Interaction Design Foundation - Lessons Created by Experts8. Selected papers from CHI, UIST, UbiComp, CSCW, and MobileHCI",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-436-history-of-computing-and-its-applications-to-domains",
      "code": "CS 436",
      "title": "History of Computing and its Applications to Domains",
      "credits": [2],
      "creditLabel": "2",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Half Semester",
      "semesters": ["Half Semester"],
      "prerequisites": [],
      "instructors": ["Rajat Moona", "Anirban Dasgupta", "Sameer G. Kulkarni"],
      "content": "Introduction: A Historical Perspective of Computer Systems - Hardware, Firmware and Software. Key inventions and Proliferations. Development of computer networks, Internet and computing platforms - the philosophy of computers, necessity and innovations, including the history and evolution of Indian ICT.Advancements: Proliferation of Computers in various domains of science and engineering, business, finance, healthcare, medicine, finance and governance. Evolution of Utility computing, Clusters, Grids, Data Centers, Cloud. Cybercrimes and Cybersecurity, Policies, Computer Ethics and Societal Impact.Future Prospects: Quantum Computing, Quantum Communication. Reflections on the lessons learnt by the History and evolution of computing.",
      "textbooks": "1. thecomputerarchive.com2. Anecdotes from the History of Modern Computing, V. Rajaraman, PHI Publishers, 2024.3. wayback.achive.orgLearning outcomes1. Students will understand the concept of Computing Systems and their evolution. 2. Students will learn about the key necessities that led to the development of different computing systems and the associated innovations.3. Students will get acquainted with the key personalities that have contributed to shape the world of computing. 4. Students will work on the Assignments and Projects that will make them think about key research areas and future computing prospects.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-491-special-topics-in-computer-science-and-engineering",
      "code": "CS 491",
      "title": "Special Topics in Computer Science and Engineering",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "400 level",
      "lecture": null,
      "theory": null,
      "practical": null,
      "ltpLabel": null,
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-602-theory-of-computation",
      "code": "CS 602",
      "title": "Theory of Computation",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-603-computational-photography",
      "code": "CS 603",
      "title": "Computational Photography",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-605-randomized-and-approximation-algorithms",
      "code": "CS 605",
      "title": "Randomized and Approximation Algorithms",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": null,
      "semesters": [],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-606-advance-topics-in-cryptology",
      "code": "CS 606",
      "title": "Advance Topics In Cryptology",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Prof. Souradyuti Paul"],
      "content": "Pseudorandomness and the Blum-Micali generator; Pseudorandom function, GGMand cascade constructions; Number-theoretic constructions of pseudorandom functions; Privateinformation retrieval; Oblivious transfer; Garbled circuits and Yao&#39; s 2-party protocol; BGW multipartyprotocol; Elliptic curves; Pairings; ID-based encryption; Interactive proofs, Zero-knowledge proofsystems, Zero knowledge proofs of knowledge, Non-interactive zero-knowledge proofs; Sigmaprotocols, ID protocols; Universal hash functions, min-entropy, leftover hash lemma",
      "textbooks": "1. Boneh, Dan and Shoup, Victor, A Graduate Course in Applied Cryptography, 2015 (draftversion available online).2. Menezes, A.J., Oorschot, P.V., and Vanstone, S.A., Handbook of Applied Cryptography, CRCPress, 2001.3. Goldreich, Oded, Foundations of Cryptography (Basic Tools), Cambridge 2001.4. Stinson, D., Cryptography: Theory and Practice, Third Edition, CRC Press, 2005.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-607-combinatorics-with-applications-in-computer-science",
      "code": "CS 607",
      "title": "Combinatorics with Applications in Computer Science",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Neeldhara Misra"],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-608-optimization-methods-for-machine-learning",
      "code": "CS 608",
      "title": "Optimization Methods For Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Prof. Dinesh Garg"],
      "content": "Optimization Methods for Machine Learning",
      "textbooks": "Optimization Methods for Machine Learning",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-609-advanced-data-structures",
      "code": "CS 609",
      "title": "Advanced Data Structures",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Prof.Manoj Guota"],
      "content": "CS 609 Advanced Data Structures",
      "textbooks": "CS 609 Advanced Data Structures",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-610-algorithms",
      "code": "CS 610",
      "title": "Algorithms",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Anirban Dasgupta"],
      "content": "Algorithms",
      "textbooks": "Algorithms",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-612-computer-systems",
      "code": "CS 612",
      "title": "Computer Systems",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Manu Awasthi"],
      "content": "Computer Systems",
      "textbooks": "Computer Systems",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-613-natural-language-processing",
      "code": "CS 613",
      "title": "Natural Language Processing",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["na"],
      "content": "Natural Language Processing",
      "textbooks": "Natural Language Processing",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-614-advanced-algorithms",
      "code": "CS 614",
      "title": "Advanced Algorithms",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": "Advanced Algorithms",
      "textbooks": "Advanced Algorithms",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-615-advanced-computer-networks",
      "code": "CS 615",
      "title": "Advanced Computer Networks",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Sameer Kulkarni"],
      "content": "This course will introduce advanced topics and recent trends in Computer Networks, Data CenterNetworks and Future Internet.Network Softwarization: SDN and NFV:Software Defined Networking (SDN): Control and Data plane separation. Network Operating System(SDN Controllers), Intent based networking (IBN), Southbound Interface (OpenFlow) and OpenVSwitch.Programmable Networking Devices: Programmable Packet Processing Pipeline (P4), Smart switches, andPRogrammable Network Interface Cards (SmartNICs).Serverless Computing - Introduction to Function as a Service (FaaS), Serverless computing, AWSLambda, OpenShift, and Knative.Data Center Networking: Networking topologies (Fat-Tree, Clos, Leaf-Spine) Docker and ContainerNetworking Interface (CNIs), Kubernetes.Network Virtualization, Virtual I/O (VirtIO), Single Root - I/O Virtualization (SR-IOV), and Network FunctionVirtualization (NFV).Kernel Bypass Techniques: Netmap, Data Plane Development Kit (DPDK), PF_Rings, and in-kernelnetworking.Zero-copy networking: Data-Direct I/O technology (DDIO), Remote-Direct-Memory-Access(RDMA)/iWARP, RDMA over Converged Ethernet (RoCE).Future Internet and Alternatives to TCP/IP:5th Generation Mobile Networks (5G), Internet of Things (IoT) and Vehicular Networking,Information Centric networking (ICN) and Named Data Networking (NDN),Advanced networking security and Cyber security challenges in current and Future Internet:Trusted computing and Confidential computing, including study on Intel Virtual-Machine Extension (VMX)and Intel software-Guard Extension (SGX) technologies.Relevant state-of-the art research works in the associated areas published in venues like SIGCOMM,NSDI, Infocom, CoNext and other top networking conferences will be discussed.Case Studies: DPDK (Data Plane Development Kit)/OpenNetVM platform, Docker Containers, Unikernels,Kubernetes.Network Simulators/Emulators: Mininet, NS-3",
      "textbooks": "1. Peterson Cascone, Brain O' Connor, Thomas Vachuska and Bruce Davice, Software-DefinedNetworks: Creative Commons, 2020 (online book). (Recommended)2. John Day, Patterns in Network Architecture: Returns to Fundamentals 2nd Edition, Prentice Hall2Publishers, 2008.Additional Reference Books:3. James Kuross and Keith Ross, Computer Networking A Top Down Approach 7 th Edition, PearsonPublishers, 2017.4. Andrew S. Tanenbaum and David J. Wetherall, Computer Networks, 5 th Edition, Pearson PublishersRecursive Books, 2014.5. Larry L. Peterson, and Bruce S.Davie, Computer Networks: A Systems Approach, 5 th Edition, MorganKaufmann, 2012.6. Richard Stevens, UNIX Network Programming 2 nd Edition, Pearson Publishers, 2015Other Online References:7. TCP/IP Technical Overview, Tutorial and Guide,a. Charles M. Kozierok, Available Online - http://www.tcpipguide.com/b. TCP/IP Tutorial and Technical Overview, (IBM Redbook) - DownloadFrom http://www.redbooks.ibm.com/abstracts/gg243376.html 8. Request for Comments (RFC) - IETF - http://www.ietf.org/rfc.html",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-616-distributed-systems-and-cloud-computing",
      "code": "CS 616",
      "title": "Distributed Systems and Cloud Computing",
      "credits": [5],
      "creditLabel": "5",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 1,
      "ltpLabel": "3-0-1",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["CSE Faculty"],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-617-computational-complexity-theory",
      "code": "CS 617",
      "title": "Computational Complexity Theory",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Balagopal Komarath", "Bireswar Das"],
      "content": "Course contents:Fundamentals: Definition of Turing Machines, Time and Space, Diagonalization, Hierarchy Theorems.Poly-time (P), Nondeterministic poly-time (NP), and completeness: Definitions of P and NP, Turing reductions and many-one reductions, NP-completeness of Satisfiability (SAT).Space complexity: Logspace (L) and Nondeterministic Logspace (NL), Savitch's Theorem, logspace reductions, NL-completeness of directed graph reachability, NL=coNL (coNL indicating complement of NL), PSPACE (polynomial space).Randomness and Interaction: Bounded-error probabilistic polynomial-time (BPP) and randomized-polynomial time (RP), Polynomial Identity Testing (PIT), Valiant-Vazirani Theorem, Polynomial hierarchy (PH) and its characterizations, PSPACE (polynomial space), PSPACE-completeness of Quantified Boolean Formulas (QBF), interactive proofs (IP), Arthur-Merlin classes, IP = PSPACE.Non-uniformity: Boolean circuits, Nick's class (NC), Matching in Randomized NC (RNC), Karp-Lipton Theorem.Cryptography and Complexity: One-way functions and Pseudorandom generators.Counting: #P, Toda's theorem, The permanentCircuits: Alternating circuits of constant depth (AC0), Alternating circuits with counting (ACC0), Threshold circuits (TC0), Nick's class level 1 (NC1) etc., AC0 lower bounds.Branching programs: Barrington's Theorem, Monoid programs.Exact complexity: Exponential-time hypothesis (ETH) and Strong exponential-time hypothesis (SETH), sparsification lemma.Randomness vs Hardness: Derandomization implies circuit lower bounds.",
      "textbooks": "Boaz Barak and Sanjeev Arora, Computational Complexity: A Modern Approach, Cambridge University Press, 2009. (Textbook)Oded Goldreich, Computational Complexity: A Conceptual Perspective, 1st Edition, Cambridge University Press, 2008. (Textbook/Reference).Other References:Stasys Jukna, Boolean Function Complexity, 1st Edition, Springer, 2012.Ding-Zhu Du and Ker-I Ko, Theory of Computational Complexity, Wiley-Blackwell, 2000.Ingo Wegener, The Complexity of Boolean Functions, 1st Edition, Wiley, 1987.Heribert Vollmer, Introduction to Circuit Complexity: A Uniform Approach, 1st Edition, Springer, 1999.Oded Goldreich. Foundations of Cryptography, Volume I: Basic Tools, Cambridge University Press, 2005",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-618-theoretical-foundations-of-machine-learning",
      "code": "CS 618",
      "title": "Theoretical Foundations of Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [
        "Anirban Dasgupta",
        "Nipun Batra",
        "Mayank Singh",
        "Shanm"
      ],
      "content": "Concentration of measure - various concentration inequalities; PAC learning,VC dimension, Rademacher complexity, generalization bounds, sample complexity.Generalization in deep learning setting - double descent; Optimization - random projection,randomized numerical linear algebra and its applications in efficient optimization (e.g. regression,matrix factorization, second order descent); Beyond the worst-case analysis of machine learningalgorithms - algorithms for non-negative matrix factorization, tensor decomposition, sparserecovery, dictionary learning; Kernels and their applications in ML --- kernel trick, Representertheorem, Support vector machine with kernels, Green's theorem, Mercer's theorem, Bochner'stheorem; learning kernels; Subset selection and its variants --- using submodularity, usingimportance sampling (coresets). Applications in data valuation, continual learning, and inoptimization; Introduction to Gaussian Process (GP), neural network-GP equivalence, neuraltangent kernel; Learning in stochastic settings -- Introduction to online learning and multi-armedbandits; Gittin's index, Thomson sampling, adversarial bandits, contextual bandits.",
      "textbooks": "Understanding machine learning, from theory to algorithms. Shai Shalev Shwartz andShai Ben David.; Learning with Kernels, Bernard Schoelkopf and Alex Smola.; Lectures on Randomized Numerical Linear Algebra. Michael Mahoney and PetrosDrineas. https://arxiv.org/abs/1712.08880; Algorithmic Aspects of Machine Learning, Ankur Moitra.https://people.csail.mit.edu/moitra/docs/bookex.pdf; Theoretical Deep Learning, lecture notes. Matt Telgarsky. https://mjt.cs.illinois.edu/dlt; Lecture notes on bandits by Alex Slivkins. https://arxiv.org/abs/1904.07272; Foundations of Machine Learning by Mohri, Rostamizadeh, Talwalkar.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-619-cs-theory-toolkit",
      "code": "CS 619",
      "title": "CS Theory Toolkit",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Bireswar Das"],
      "content": "Logic: Propositions, connectives, quantifiers, rules of inference, induction, strong induction, structural induction.Combinatorics: Definition of graphs and various structures in graphs. Koenig, Hall, and Menger theorems. The TONCAS (The obvious necessary conditions are also sufficient) principle, Common proof techniques (structural induction, attacking minimal counter-examples etc.) for graph theory, algebraic techniques, algorithmic proofs (Havel-Hakimi theorem, Eulerian circuits, Pruefer codes). Advanced Topic: Parameterized algorithms: Branching algorithms and proof by cases, refining cases to increase efficiency, tree decompositions and dynamic programming (Independent set), algebraic techniques for exact algorithms (inclusion-exclusion)Probability theory: Review of basics, Expectation and variance, linearity of expectation, concentration bounds, derandomization, probabilistic methods in proofs. Advanced topic: A randomized algorithm for perfect matching.Computational models: What is computation?, motivation and definition of RAM (Random access machine) model, Algorithmic correctness and worst-case complexity. lower bounds vs upper bounds, reductions, Satisfiability and reductions to Satisfiability (Hamiltonian Cycle to Satisfiability, Vertex Cover to Satisfiability), hardness and completeness via reductions. identifying the right reductions (many-one, Turing, logspace etc.), gadget constructions (one or two advanced examples). Advanced topic: Boolean circuits, constant-depth circuit constructions, circuit reductions.",
      "textbooks": "Discrete Mathematics and Its Applications, Kenneth H Rosen.; Introduction to Graph Theory, Douglas B West; Mathematics and Computation, Avi Wigderson; Randomized Algorithms, Raghavan, Motwani; https://courses.csail.mit.edu/6.892/spring19/ (Hardness proofs, Course from MIT)",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-620-incentives-and-machine-learning",
      "code": "CS 620",
      "title": "Incentives and Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["P Manisha"],
      "content": "Course contents: 1. Introduction to Incentives a. Multi-agent Interactions and Equilibriumb. Mechanism Design - VCGc. Optimal Auctionsd. Ad Auctions2. Machine Learning in Incentive Design a. Automated Mechanism Design (AMD) - Optimal Auctions through Deep Learning (DL)b. AMD - Redistribution Mechanismsc. Iterative Combinatorial Auctions using DL and MLd. Multi-Armed Bandit-based Mechanism Design3. Incentive Aware Machine Learning a. Strategic Classificationb. Incentive Aware PAC-Learningc. Incentivizing Differential Privacy in ML4. Collaborative Machine Learning a. Federated Learningb. Incentives to Collaborate in Federated Learning5. Data Qualitya. Data Valuation and Crowdsourcingb. Peer Prediction for Responsible Data Governance",
      "textbooks": "Game Theory and Mechanism Design, Y Narahari. World Scientific Publishing Company, 2014.; Algorithmic Game Theory, edited by Noam Nisan, Tim Roughgarden, Eva Tardos and Vijay Vazerani. (Non-printable version available online).; Chang, J. Morris, et al. Privacy-Preserving Machine Learning. Simon and Schuster, 2023.; Yang, Qiang, Lixin Fan, and Han Yu, eds. Federated learning: Privacy and incentive. Vol. 12500. Springer Nature, 2020.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-621-ethics-of-ai",
      "code": "CS 621",
      "title": "Ethics of AI",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["P Manisha", "Anirban Dasgupta"],
      "content": "Algorithmic Fairness and Accountability, where students study operationalized fairness criteria, constrained optimization for fair models, and the socio-technical dimensions of bias through case studies in vision and NLP; the Interpretability and Explainability module introduces inherently interpretable models, post-hoc explanations (LIME, SHAP, Integrated Gradients, SmoothGrad), counterfactual and concept-based explanations (TCAV, Network Dissection), and connects interpretability with fairness; Mechanistic Interpretability and Representation Engineering delves into circuits, compiled transformers, and emergent reasoning in large models (e.g., Chain-of-Thought prompting, interpretability in diffusion and vision-language models); Robustness and Adversarial ML explores vulnerabilities in AI systems, the history and theory of adversarial examples, adversarial training, robustness-accuracy trade-offs, and the security of large models under adversarial or distributional shifts; Privacy and Security in AI explores data protection, membership inference,reconstruction attacks, and the theoretical and practical aspects of Differential Privacy and DP-SGD; Ethics of Large-Scale AI Systems examines the societal, legal, and governance implications of LLMs and generative models, including transparency, accountability, and global regulatory trends",
      "textbooks": "(Main text) Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MITPress.; (Main text) Dwork, C., & Roth, A. (2014). The Algorithmic Foundations of Differential Privacy. Foundations and Trends® inTheoretical Computer Science, 9(3-4), 211-407.; (Main text) Molnar, Christoph. Interpretable machine learning, 2019.; Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. Proceedings ofInnovations in Theoretical Computer Science (ITCS).; Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., & Carter, S. (2020). An overview of circuits. Distill.https://distill.pub/2020/circuits/; Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differentialprivacy. Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS).; European Commission. (2024). The EU Artificial Intelligence Act: A Regulatory Framework for Trustworthy AI. Official Journal ofthe European Union",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-special-topics-in-computer-science",
      "code": "CS 691",
      "title": "Special Topics in Computer Science",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [
        "Anirban Dasgupta",
        "Nipun Batra",
        "Mayank Singh",
        "Shanm"
      ],
      "content": "Special Topics in Computer Science",
      "textbooks": "Special Topics in Computer Science",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-i-special-topics-in-computer-science-social-networks",
      "code": "CS 691-I",
      "title": "Special Topics in Computer Science: Social Networks",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Neeldhara Misra"],
      "content": "Introduction Handling Real-world Network DatasetsStrength of Weak TiesStrong and Weak Relationships (Continued) & Homophily Homophily Continued and +Ve / -Ve Relationships Link Analysis Cascading Behaviour in NetworksLink Analysis (Continued) Power Laws and Rich-Get-Richer Phenomena Power law (contd..) and Epidemics Small World PhenomenonPseudocore (How to go viral on web)",
      "textbooks": "1. Networks, Crowds and Markets by David Easley and Jon Kleinberg, Cambridge University Press, 2010 (available for free download).2. Social and Economic Networks by Matthew O. Jackson, Princeton University Press, 2010.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-ii-special-topics-in-computer-science-computer-algebra",
      "code": "CS 691-II",
      "title": "Special Topics in Computer Science: Computer Algebra",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Bireswar Das"],
      "content": "Special Topics in Computer Science: Computer Algebra",
      "textbooks": "Special Topics in Computer Science: Computer Algebra",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-iii-special-topics-in-computer-science-learning-from-data",
      "code": "CS 691-III",
      "title": "Special Topics in Computer Science: Learning from Data",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Shanmuganathan Raman"],
      "content": "The course will involve reading of the book by Gilbert Strang on the topic followed by presentations on individual topics for evaluation.",
      "textbooks": "The course will involve reading of the book by Gilbert Strang on the topic followed by presentations on individual topics for evaluation.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-iv-special-topics-in-computer-science-advanced-machine-learning",
      "code": "CS 691-IV",
      "title": "Special Topics in Computer Science: Advanced Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Anirban Dasgupta", "Nipun Batra"],
      "content": "Learning Theory Reinforcement Learning Deep Learning Bayesian optimization Graphical models",
      "textbooks": "Advanced Machine Learning",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-ix-special-topics-in-computer-science-ubiquitous-computing",
      "code": "CS 691-IX",
      "title": "Special Topics in Computer Science: Ubiquitous Computing",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Nipun Batra"],
      "content": "The aim of this class will be to introduce the students to ubiquitous computing. We will focus on howtraditional topics of computing have evolved to support the vision of a connected, portable, and a human-centric computing environment. Thus, this course will discuss both the classic and recent view ofubiquitous computing across varied application domains.The course will include discussion of the contributions of various fields, including (given the highinterdisciplinary nature of the topics, some of the subtopics span multiple topics):● human-computer interaction: novel interfaces for interacting with the computing world, eg.gestural, haptic feedback, augmented reality, wearables, pervasive displays, etc.● embedded computing: wearable computing, edge computing, self-powered systems, intermittentpowered systems, energy harvesting, duty cycling, etc.● computer vision: activity sensing based on thermal cameras, multi-spectral imaging, etc.● distributed systems: load scheduling and balancing, edge intelligence, context and contentsummarization, cloud computing.● machine learning: applications of recent advances in processing (mostly) structured time-seriesdata towards mostly supervised and unsupervised learning problems, eg. predicting location of auser from the WiFi signal strength (regression problem); unsupervised activity recognition(clustering); edge machine learning.The students will gain practical experience in developing sensing systems in different application domains,such as:● activity recognition: sensing walking gait, activity (walking, sleeping), etc.● health sensing: sensing lung health, breathing, eating, stress, etc.● gestural interaction: novel gestural control and devices like smart rings, smart watches, andinteracting with them over varied interfaces, eg. doppler effect based web page browsing,interacting via the jaw muscle movement.● speech interaction: conversational agents, novel earables (ear wearables)● fabrication and wearables: novel fabrication technologies, novel wearables for detecting activitiesof daily life (mentioned above)● localization: localizing multiple people inside and outside home using novel sensing technologiessuch as backscatter, multi-path, etc.",
      "textbooks": "Given the evolving nature of the topic, there is no prescribed textbook on the course. However, the followingis a select set of similar offerings from top universities across the world.● Physical computing course at UW offered by Jon Froehlich: https://makeabilitylab.github.io/physcomp/● Ubiquitous computing course at UW by Mayank Goel:https://www.mayankgoel.courses/teaching/ee590p-fall-2015/2● Physical computing course at NYU offered by Tom Igoe: https://itp.nyu.edu/physcomp/● Ubiquitous computing course at CMU: https://www.hcii.cmu.edu/courses/ubiquitous-computingThe course readings are based on a mix of classic and recent papers in top CS conferences such as:● ACM Ubicomp: https://ubicomp.org/● ACM Sensys: http://sensys.acm.org● ACM IPSN: https://ipsn.acm.org● ACM CHI: https://chi2021.acm.org● ACM UIST: https://uist.acm.org/uist2021/● IEEE Pervasive Computing: https://www.computer.org/csdl/magazine/pc",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-v-special-topics-in-computer-science-graph-theory-and-graph-algorithms",
      "code": "CS 691-V",
      "title": "Special Topics in Computer Science: Graph Theory and Graph Algorithms",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Anirban Dasgupta", "Bireswar Das", "Manoj Gupta"],
      "content": "Special Topics in Computer Science: Graph theory and graph algorithms",
      "textbooks": "Special Topics in Computer Science: Graph theory and graph algorithms",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-vi-special-topics-in-computer-science-parameterized-algorithms",
      "code": "CS 691-VI",
      "title": "Special Topics in Computer Science: Parameterized Algorithms",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Neeldhara Misra"],
      "content": "Special Topics in Computer Science: Parameterized Algorithms",
      "textbooks": "Special Topics in Computer Science: Parameterized Algorithms",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-vii-special-topics-in-computer-science-complexity-theory",
      "code": "CS 691-VII",
      "title": "Special Topics in Computer Science: Complexity Theory",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Prof. Bireswar and Prof. Balagopal"],
      "content": "Special Topics in Computer Science: Complexity Theory",
      "textbooks": "Special Topics in Computer Science: Complexity Theory",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-viii-special-topics-in-computer-science-algebraic-complexity-theory",
      "code": "CS 691-VIII",
      "title": "Special Topics in Computer Science: Algebraic Complexity Theory",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Balagopal Komarath"],
      "content": "Special Topics in Computer Science: Algebraic Complexity Theory",
      "textbooks": "Special Topics in Computer Science: Algebraic Complexity Theory",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-x-special-topics-in-computer-science-deep-learning",
      "code": "CS 691-X",
      "title": "Special Topics in Computer Science: Deep Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Shanmuganathan Raman"],
      "content": "Special Topics in Computer Science: Deep learning",
      "textbooks": "Special Topics in Computer Science: Deep learning",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xi-special-topics-in-computer-science-learning-with-graphs",
      "code": "CS 691-XI",
      "title": "Special Topics in Computer Science: Learning with Graphs",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [
        "Manoj Agarwal",
        "Anirban Dasgupta",
        "Nipun Batra",
        "Shan"
      ],
      "content": "Graph clustering: spectral clustering, clustering for streaming graphsRandom walks: convergence properties, connections with eigenvalues, Pagerank and its variants, personalized PageRank, MCMC, clustering using random walksGraph sampling and estimation: estimating number of nodes, edges, triangles, small cliques; graph sparsificationKnowledge graphs: schema, how to create from unstructured data, inference algorithms, reasoning over knowledge graphs.Neural networks for graphs: initial models (w/o GNNs), node representations using graphlets, graph kernels, representations using random walks; Graph Neural networks: node classification, edge prediction/classification, graph classification; training - message passing, training on large graphs using gradients, Weisfeler-Lehman test, dynamic graphs - GraphSAGE, graph convolutional networks; generative models using GNNs. applications of GNNs: NLP - text classification and neural machine translation, image classifications, applications in physics and chemistry, applications in combinatorial optimization",
      "textbooks": "William Hamilton, Graph Representation Learning. Morgan and Claypool. Pre-publication draft available at https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon KleinbergNetwork Science by Albert-László Barabási",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xii-special-topics-in-computer-science-probabilistic-machine-learning",
      "code": "CS 691-XII",
      "title": "Special Topics in Computer Science: Probabilistic Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Prof. Nipun Batra"],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xiii-special-topics-in-computer-science-transformers-and-gnns",
      "code": "CS 691-XIII",
      "title": "Special Topics in Computer Science: Transformers and GNNs",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Prof. Manoj Aggarwal"],
      "content": "Course contents:Word Representation in Vector Space- Representation of words and phrases, Negative Sampling; Algebraic structure of Word Sense - Dimensionality of Word EmbeddingsAnatomy of a Transformers - attention mechanism. Text Generation: Greedy Search Decoding, Beam Search Decoding, Sampling Methods, Top-K, and Nuclear Sampling, Which Decoding Method is Best?Summarization & Question AnsweringKnowledge distillation and QuantizationDealing with few to no labelsTransformers and GNNs - relation between the architectures, Graph transformer networks.Graph Neural Networks GNN Model and Design Space, Theory of Graph Neural NetworksKnowledge Graph Embeddings, Reasoning over Knowledge GraphsApplications of Graph Neural Networks: Frequent Subgraph Mining with GNNs, GNNs for Recommender Systems, Community Structure in Networks",
      "textbooks": "Text Books:William Hamilton, Graph Representation Learning. Morgan and Claypool. Pre-publication draft is available at https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf Network Science by Albert-László BarabásiRecent surveys, e.g., https://arxiv.org/abs/2106.06090 Recent papers will be circulated in class.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xiv-special-topics-in-computer-science-usable-privacy-and-security",
      "code": "CS 691-XIV",
      "title": "Special Topics in Computer Science: Usable Privacy and Security",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Abhishek Bichhawat"],
      "content": "The aim of this course is to provide a user-focused view of security and privacy in systems. This wouldinclude topics related to secure systems and their usability analysis. We will discuss the state-of-the-artworks in the area of usable security to understand how and why usability is an important concern whendesigning systems. Some of the potential topics of interest are:● Introduction to usability with security and privacy● Usable encryption: usability of classical encryption techniques and cryptosystems● Measurement Studies: usability measurement, design of user studies, techniques - traditional andmodern, quantitative analysis techniques, statistical analysis, ethical concerns, issues and biasesin studies● Usability case studies: password management, two-factor authentication, anonymity, developer-oriented studies● User perspectives of security and privacy on the web and social media: social engineering,phishing● Data privacy regulationsThe students would perform user studies involving institute personnel for different problem statements aspart of a capstone course project.",
      "textbooks": "There is no prescribed textbook for the course. We will be following research publications appearing atSOUPS, PETS, ACM CHI, Usenix Security, and similar conferences or symposiums.To understand methods for conducting usability studies, we recommend this book:● \"Research Methods in Human-Computer Interaction\" by Jonathan Lazar, Jinjuan Heidi Feng, HarryHochheiser.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xix-special-topics-in-computer-science-ai-for-social-good",
      "code": "CS 691-XIX",
      "title": "Special Topics in Computer Science: AI for Social Good",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Nipun Batra", "Udit Bhatia"],
      "content": "Topics will span a wide array of AI domains such as but limited to:- Healthcare: AI for diagnosis, personalised medicine, public health monitoring, and remote care- Environment and Sustainability: AI in climate modelling, pollution monitoring, energy efficiency, and conservation- Disaster Resilience: AI for disaster prediction, response, and resource management- Education: Personalized learning systems, intelligent tutoring, analysis of educational outcomes- Public Safety and Security: Social engineering, AI for detecting misinformation, predictive policingThe AI technologies would include:- Supervised Learning: Applications in classification and prediction tasks (e.g., disease diagnosis, fraud detection, poverty prediction)- Unsupervised Learning: Clustering and anomaly detection for environmental monitoring, resource allocation- Semi-supervised Learning: Addressing challenges with limited labelled data in social good applications- Reinforcement Learning: Applications in resource optimization, healthcare interventions, and adaptive learning systems- Natural Language Processing (NLP): Sentiment analysis, misinformation detection, speech recognition for social applications- Computer Vision: Object detection, segmentation and classification for environmental monitoring, healthcare imaging- Deep Learning: Use of neural networks for complex social problems (e.g., medical imaging, disaster prediction)",
      "textbooks": "There is no prescribed textbook for the course. We will be following research publications appearing at AAAI, IJCAI, KDD, Neurips, ICLR, and similar top conferences or symposiums.There are some related courses at top universities that would serve as a good reference- AI for Social Impact course at Harvard- Social good at Google- AI for Social Good course at Stanford- AI for social good coursera specialisation",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xv-special-topics-in-computer-science-understanding-large-language-models",
      "code": "CS 691-XV",
      "title": "Special Topics in Computer Science: Understanding Large Language Models",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": "Introduction:1. Word Representation in Vector Space 1.1. Representation of words and phrases 1.2. Algebraic structure of Word Sense 1.3. Dimensionality of Word Embeddings2. Text Generation 2.1 Greedy Search Decoding 2.2 Beam Search Decoding 2.3 Sampling Methods 2.4 Top-K and Nuclear Sampling 2.5 Which Decoding Method is Best?3. Knowledge distillation and Quantization4. Dealing with few to no labels5. Anatomy of a Transformers6. Transfer learningLLMs:7. Encoder only models: 7.1 Deep contextualized word representations (ELMo) 7.2 Improving Language Understanding by Generative Pre-Training (OpenAI GPT) 7.3 RoBERTa: A Robustly Optimized BERT Pretraining Approach 7.4 ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators8. Encoder-Decoder models: 8.1. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension 8.2. mT5: A massively multilingual pre-trained text-to-text transformer 8.3. AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model9. Decoder only model 9.1. Language Models are Unsupervised Multitask Learners (GPT-2) 9.2. PaLM: Scaling Language Modeling with Pathways 9.3. OPT: Open Pre-trained Transformer Language Models10. How to Adapt LLMs? 10.1. Prompting for few-shot learning 10.2. Prompting as parameter-efficient fine-tuning 10.3. In-context learning 10.4. Calibration of prompting LLMs 10.5. Reasoning 10.6. Language Models as Knowledge Bases?11. Dissecting LLMs: Data, Model Scaling and Risks (if time permits) 11.1. Training Compute-Optimal Large Language Models",
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xvi-special-topics-in-computer-science-user-studies-for-privacy-and-security",
      "code": "CS 691-XVI",
      "title": "Special Topics in Computer Science: User Studies for Privacy & Security",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 3,
      "ltpLabel": "0-0-3",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Abhishek Bichhawat"],
      "content": "The aim of this course is to perform field studies to understand the normal users' view of security and privacy in systems. The students would perform user studies involving institute personnel for different problem statements as part of the course. There would be weekly meetings to discuss the findings and driving the survey/interviews with the users for obtaining data that can be analyzed to answer the research questions posed. The course is completely project-based, which would make the students familiar with three distinct components:1. Data Collection a. Surveys/Interviews to obtain real-world data pertaining to the research question2. Data Analysisa. Qualitative and quantitative analysis of collected datab. Choosing the right data and discarding meaningless data3. Reporting a. Report the findings in a meaningful way b. Discuss the overall trends and steps to tackle the usability/security issues if any for the general society",
      "textbooks": "We will be following research publications appearing at SOUPS, PETS, ACM CHI, Usenix Security, and similar conferences or symposiums for understanding the three componentsFor conducting usability studies, we recommend this book:● \"Research Methods in Human-Computer Interaction\" by Jonathan Lazar, Jinjuan Heidi Feng, Harry Hochheiser.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xvii-special-topics-in-computer-science-combinatorial-and-computational-aspects-of-games",
      "code": "CS 691-XVII",
      "title": "Special Topics in Computer Science: Combinatorial and Computational Aspects of Games",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Prof. Neeldhara Misra"],
      "content": "Combinatorial Games: Game Trees, Zermelo's Theorem, StrategyNormal-Play Games: Positions and Their Types, Sums of Positions, EquivalenceImpartial Games: Nim, The Sprague-Grundy Theorem, Applying the MEX PrincipleHackenbush and Partizan Games: Hackenbush, Dyadic Numbers and Positions, The SimplicityPrincipleSurreal NumbersSpecific Games: Kayles and Variations, The Game of SET and SuperSET, Hex, UNO, Hanabi, Snakes andLadders, and (Meta) Tic-Tac-ToeComplexity of Games: NP-completeness and PSPACE-completeness of specific games",
      "textbooks": "1. Game Theory: A Playful Introduction, by Matt DeVos and Deborah A. Kent2. Combinatorial Game Theory, by Aaron N. Siegel3. Winning Ways for your Mathematical Plays, by Elwyn R. Berlekamp, John H. Conway, andRichard K. Guy4. Lessons in Play, by Michael H. Albert, Richard J. Nowakowski, and David Wolfe",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xviii-special-topics-in-computer-science-applications-of-linear-algebra-to-combinatorics",
      "code": "CS 691-XVIII",
      "title": "Special Topics in Computer Science: Applications of Linear Algebra to Combinatorics",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Neeldhara Misra"],
      "content": "This course explores applications of tools from linear algebra in combinatorics, geometry, algorithms, and graph theory. We will emphasize proofs and proof techniques. The goal is to use the applications as a backdrop to practice and review linear algebra skills, but also to develop a an understanding of new proof techniques and recognize how to apply them in new settings as well.",
      "textbooks": "This course explores applications of tools from linear algebra in combinatorics, geometry, algorithms, and graph theory. We will emphasize proofs and proof techniques. The goal is to use the applications as a backdrop to practice and review linear algebra skills, but also to develop a an understanding of new proof techniques and recognize how to apply them in new settings as well.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xx-special-topics-in-computer-science-cyber-physical-systems-for-industrial-applications",
      "code": "CS 691-XX",
      "title": "Special Topics in Computer Science: Cyber Physical Systems for Industrial Applications",
      "credits": [2],
      "creditLabel": "2",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Half Semester",
      "semesters": ["Half Semester"],
      "prerequisites": [],
      "instructors": ["Lalit Singh"],
      "content": "The course provides an overview of the underlying principles of this future evolution towards cyber-physical networks, whilealso discussing open research problems and societal implications of such networks.1. Cyber-Physical Systems (CPS) in the real world, CPS development life cycle, Basic principles of design and validation of CPS,Industry 4.0, AUTomotive Open System ARchitecture (AutoSAR), Industrial Internet of Things (IIOT) implications, BuildingAutomation, Case Studies of CPS: Nuclear Power Plants, etc.2. CPN Components and Functions; CPS HW platforms - Processors, Sensors, Actuators; CPS Network - WirelessHart, CampusArea Network (CAN)s, Automotive Ethernet; CPS stack - RTOS; 3. Safety Critical Systems (SCS) as CPS and Design Principles of SCS4. Modelling of CPS and iimplementation5. Formal Methods for Safety Assurance of Cyber-Physical Systems6. Testing of CPS",
      "textbooks": "Research Papers from CPS conferences",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-691-xxi-special-topics-in-computer-science-verification-and-validation-of-safety-critical-software-systems",
      "code": "CS 691-XXI",
      "title": "Special Topics in Computer Science: Verification and Validation of Safety-Critical Software Systems",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Lalit Kumar Singh"],
      "content": "Introduction to Safety Critical Systems and Industry 5.0: Fundamental concepts of safety criticalsystems along with examples (aerospace, automotive, medical devices, nuclear, industrial automation).The evolution from Industry 4.0 to Industry 5.0 - core principles: human-centric, resilience, sustainability.Key challenges in V&amp; V of safety-critical systems in Industry 5.0Fundamentals of Verification and Validation (V&amp; V): Definitions and differences: Verification vsValidation. V&amp; V life cycle in system development. Types of V&amp; V: static analysis, dynamic testing, formalverification. Role of simulation and prototyping in V&amp; V. Organization and management of the verificationand validation process.Safety Classification and Types of Software: Classification of systems, Types of software, Devicescontaining software, Software tools.Software Related Activities and Documents: Life cycle for new software: System requirementsspecification, Computer system specification, System test plans, Software design, Coding, Computersystem integration, Integrated computer system tests, Validation and commissioning tests, Systemhandover, Operation, maintenance and modification.Verification by Phase: Verification of System requirements specification, Computer system specification,System test plans, Software design, Coding, Computer system integration, Integrated computer systemtests, Validation and commissioning tests, System handover, Operation, maintenance and modification,Existing accessible software, Existing proprietary software, Configurable software, Data verification.Validation: New software, Existing accessible software, Existing proprietary software, Configurablesoftware, Data validation.Role of Artificial Intelligence in V&amp; V: Role of AI in Verification, Role of AI in Validation.Labs will include hands-on hands-on development of case studies (software systems) and theirVerification &amp; Validation, Report generation.",
      "textbooks": "Text Books:1. Safety-Critical Systems: Problems, Process and Practice by Nancy Leveson.2. Verification, Validation and Testing of Engineered Systems edited by Avner Engel.Additional Reference Books:1. Principles of Model Checking by Christel Baier and Joost-Pieter Katoen.Other Online References:1. Research papers and standards documentation from ISO, IEC, SAE, IEEE.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "cs-692-i-special-topics-in-computer-science-congestion-control",
      "code": "CS 692-I",
      "title": "Special Topics in Computer Science: Congestion Control",
      "credits": [2],
      "creditLabel": "2",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Special Topics",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Half Semester",
      "semesters": ["Half Semester"],
      "prerequisites": [],
      "instructors": ["Subir Varma", "Sameer G Kulkarni"],
      "content": "Summary of the course:A current and concise exposition of some of the most important topics in the area of congestion control for computer networks, with special emphasis on analytical modeling of congestion control systemsAccessible coverage of the advanced topics of optimization and control theory as applied to congestion control systemsApplication of the theory to the problem of congestion control in a number of areas such as data center networks, video streaming, high-speed links, and broadband wireless networks.Course contents:Introduction to Congestion Control: Chiu-Jain result on the optimality of AIMD control, Fundamental congestion control algorithms such TCP Reno, TCP Vegas and RED based Active Queue Management.Congestion Control Models: Models using fluid approximations, Models using stochastic theory, \"Square Root\" formula for throughput of TCP, Generalization to AIMD congestion control schemes, Optimization Theory and Control Theory, Nyquist Criterion.Congestion Control in Broadband Wireless and High Speed Networks: TCP over wireless links, high-speed networks with long latencies. Congestion Control in Data Center Networks Congestion Control in Video Delivery: HTTP Adaptive Streaming (HAS) Congestion Control in Ethernet Networks: IEEE802.1Qau or Quantum Congestion Notification (QCN) Emerging Topics: Use of Machine Learning to design advanced control algorithms.",
      "textbooks": "\"Internet Congestion Control\" by Subir Varma, Morgan Kauffman/Elsevier, (2015).",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-613-mathematical-models-for-data-science",
      "code": "EM 613",
      "title": "Mathematical Models for Data Science",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-614-advanced-probability-and-statistics",
      "code": "EM 614",
      "title": "Advanced Probability and Statistics",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-615-programming-for-data-science",
      "code": "EM 615",
      "title": "Programming for Data Science",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-616-time-series-analysis",
      "code": "EM 616",
      "title": "Time Series Analysis",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-618-optimization-for-data-science-and-machine-learning",
      "code": "EM 618",
      "title": "Optimization for Data Science and Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-619-machine-learning-for-predictive-analysis",
      "code": "EM 619",
      "title": "Machine Learning for Predictive Analysis",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-621-big-data-modeling-and-management-systems",
      "code": "EM 621",
      "title": "Big Data Modeling and Management Systems",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-622-generative-ai-with-large-language-models",
      "code": "EM 622",
      "title": "Generative AI with Large Language Models",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-623-advanced-data-visualization-techniques",
      "code": "EM 623",
      "title": "Advanced Data Visualization Techniques",
      "credits": [2],
      "creditLabel": "2",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Half Semester",
      "semesters": ["Half Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-624-risk-and-decision-analysis",
      "code": "EM 624",
      "title": "Risk and Decision Analysis",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "em-627-spatial-data-science-and-applications",
      "code": "EM 627",
      "title": "Spatial Data Science and Applications",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Data Science for Decision Making",
      "program": "Data Science for Decision Making",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": null,
      "textbooks": null,
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-112-computing",
      "code": "ES 112",
      "title": "Computing",
      "credits": [3],
      "creditLabel": "3",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "100 level",
      "lecture": 2,
      "theory": 0,
      "practical": 2,
      "ltpLabel": "2-0-2",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": "Elementary Programming TechniquesBuilding familiarity with instruction driven task completion using Scratch or CS Unplugged.Von Neumann architecture, the translation process for a program (Source code -> Interpreter/Compiler -> Machine code)Expressions: values or literals, combining values using operators, basic types (int, float, str, bool); type conversion; lazy evaluation for Boolean operators.Functions (scope), examples from math and random; writing your own simple functions that do not require control flow; (e.g, \"celsius_to_fahrenheit\")Program development lifecycle (edit, test, debug; libraries such as pytest, simple_benchmark; and tools such as flake8)Control flow: conditionals (if-else, match); loops (while, for).Advanced Data TypesTuples: construction, unpacking.Lists: construction (+ comprehensions), indexing, slicing.Sets: construction, selection, set operations.Dictionaries: construction, indexing, update, merging.Complex data (dictionaries of lists to lists etc.); frozenset, frozendict.Objects and ClassesExceptions: try-catch-finally, writing your own exceptions.Advanced Programming TechniquesRecursion (basic examples such as Fibonacci and Towers of Hanoi)Functions as values (functools)Iterators and generators (itertools, more-itertools)Programming in the LargeModules: code organization Selection of python modules (e.g, re, numpy, scipy, beautifulsoup, flask, etc).",
      "textbooks": ".The official Python tutorial and referenceDive Into Python 3, Mark PilgrimLearn Python the hard way, Zed A ShawA byte of Python, Swaroop CHAutomate the boring stuff with Python, Al Sweigart",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-119-principles-of-artificial-intelligence",
      "code": "ES 119",
      "title": "Principles of Artificial Intelligence",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "100 level",
      "lecture": 3,
      "theory": 0,
      "practical": 3,
      "ltpLabel": "3-0-3",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Anirban Dasgupta", "Krishna P Miyapuram", "Manisha P"],
      "content": "Introduction: Historical Perspectives, Computational Complexity and Computational Infeasibility, Problem Solving, Sorting and Searching, Games and Adversarial Search; Stochastic Systems and Decision Making: Uncertainty in AI, Bayesian Networks, Learning & Inference, Decision Theory, Markov Decision Processes, Decision Trees, Knowledge Graphs, Expert Systems, Precision and Recall, Models of Learning: Supervised and Unsupervised learning (including, dimensionality reduction and clustering), Reinforcement learning, Neural Networks, Introduction to Deep Learning. AI Applications: GenAI, Computer Vision, Natural Language Processing, Speech, Robotics.Labs: Understand asymptotic complexity by examples, differentiating between sublinear, poly, exp, etc.; Implement search and adversarial search, eg. a full game tree; Implement decision trees, random forests, clustering; Intro to Pytorch, llama, and scikit-learn; Make a SLM; Object Detection and Classification (YOLO etc.).",
      "textbooks": "Primary Textbook1. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. 4th Edition, Pearson, 2021.Additional Reference Books1. Elaine Rich, Kevin Knight, and Shivashankar B. Nair. Artificial Intelligence. 3rd Edition, McGraw-Hill, 2008.2. Nils J. Nilsson. The Quest for Artificial Intelligence. Cambridge University Press, 2009.3. Wolfgang Ertel. Introduction to Artificial Intelligence. 2nd Edition, Springer, 2017.Sejnowski, Terrence J. The Deep Learning Revolution. MIT Press, 20185. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.6. Mitchell, T. M. (1997). Machine learning. McGraw-Hill.7. James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: With applications in Python (pp.15-67). Springer International Publishing.8. Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification (2nd ed.). John Wiley & Sons.9. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.10. Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.11. Murphy, K. P. (2022a). Probabilistic machine learning: An introduction. MIT Press.12. Murphy, K. P. (2022b). Probabilistic machine learning: Advanced topics. MIT Press.13. Prince, S. J. D. (2023). Understanding deep learning. MIT Press.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-203-digital-systems",
      "code": "ES 203",
      "title": "Digital Systems",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "200 level",
      "lecture": 2,
      "theory": 1,
      "practical": 3,
      "ltpLabel": "2-1-3",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Joycee Mekie"],
      "content": "Digital Systems for EE & CSE",
      "textbooks": "Digital Systems for EE & CSE",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-214-discrete-mathematics",
      "code": "ES 214",
      "title": "Discrete Mathematics",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "200 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Neeldhara Mishra"],
      "content": "cs 3222 of 2Context:ZO10 Replaces the course CS 322 Discrete Mathematics approved in the 4th Senate Discrete Mathematics",
      "textbooks": "cs 3222 of 2Context:ZO10 Replaces the course CS 322 Discrete Mathematics approved in the 4th Senate Discrete Mathematics",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-215-computer-organization-and-architecture",
      "code": "ES 215",
      "title": "Computer Organization and Architecture",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "200 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Manu Awasthi"],
      "content": "Computer Organization and Architecture",
      "textbooks": "Computer Organization and Architecture",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-242-data-structures-and-algorithms-i",
      "code": "ES 242",
      "title": "Data Structures and Algorithms I",
      "credits": [3],
      "creditLabel": "3",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "200 level",
      "lecture": 2,
      "theory": 0,
      "practical": 2,
      "ltpLabel": "2-0-2",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Manoj Gupta"],
      "content": "Data Structures and Algorithms - I",
      "textbooks": "Data Structures and Algorithms - I",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-242-n-data-structures-and-algorithms-i",
      "code": "ES 242(N)",
      "title": "Data Structures and Algorithms I",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "200 level",
      "lecture": 3,
      "theory": 0,
      "practical": 2,
      "ltpLabel": "3-0-2",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [
        "Birsewar Das",
        "Anirban Dasgupta",
        "Manoj Gupta",
        "Neeldh"
      ],
      "content": "C/C++: Arrays, Pointers and Structures, Multidimensional Arrays, Dynamic memory allocation. Quicksort, Mergesort, Heapsort; Basic notions of asymptotics, Stacks, Simulation of Recursive algorithms using stacks; Queues, Priority queues; Linked lists; Trees. Binary trees, Node representation, Tree traversals. Binary search, Binary search trees; Graphs; Adjacency matrix, adjacency list representation, Depth first search, Breadth first search; Minimum Spanning Tree, Single Source Shortest Path.",
      "textbooks": "Y. Langsam and A. M. Tannenbaum, Data Structures using C and C++, PHI, 1998.T. Cormen, C. E. Leiserson, R. Rivest and C. Stein, Introduction to Algorithms (3E), PHI, 2010.A. Aho, J. Hopcroft, J. Ullman, Data Structures and Algorithms (1E), Pearson, 2002.M. T. Goodrich and R. Tamassia, Algorithm Design: Foundations, Analysis and Internet Examples, Wiley, 2006.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-248-introduction-to-data-structures-and-algorithms",
      "code": "ES 248",
      "title": "Introduction to Data Structures and Algorithms",
      "credits": [3],
      "creditLabel": "3",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "200 level",
      "lecture": 1,
      "theory": 0,
      "practical": 3,
      "ltpLabel": "1-0-3",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["CSE faculty"],
      "content": "Basics of C and Data Structures: Data types: primitive and user-defined data types in C/C++, including those portabletypes used in embedded systems; Arrays, Pointers and Strings, Functions; Stacks, Queues, Linked List; MemoryManagement: Allocation, De-allocation, Memory Layout; Time and Space Complexity; Data Processing: Sorting andSearching; Data Handling and Processing: File Handling and Input/Output Handling, Files vs memory: buffering, streams,Standard Input/Output in C (fopen,fread, fwrite, fprintf, scanf pitfalls), Hands-on: implement reading/writing large filesefficiently. Working with Structured and Unstructured Data: Handling Comma Separated Values/Tab Separated Valuesand binary tabular data in C, String parsing for unstructured text., Introduction to record-oriented storage (fixed vs variable length)",
      "textbooks": "Textbooks:1. Charles Severance, \"C Programming for Everybody\", Online (https://www.cc4e.com/)2. Michael Barr and Andy Oram. 1998. \"Programming Embedded Systems in C and C++\" (1st. ed.). O'Reilly & Associates, Inc., 3. USA.4. Yedidyah Langsam, Moshe J. Augenstein, and Aaron M. Tenenbaum. 1996. Data structures using C and C++ (2nd ed.).Prentice Hall Press, USA.5. K. N. King. 2008. C Programming: A Modern Approach, Second Edition. W.W. Norton & Company.6. Peter Pacheco. 2011. An Introduction to Parallel Programming (1st. ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.References:1. The C Programming Language by Brian Kernighan and Dennis Ritchie2. David R. Butenhof. 1997. Programming with POSIX threads. Addison-Wesley Longman Publishing Co., Inc., USA.3. Michael J. Quinn. 2003. Parallel Programming in C with MPI and OpenMP. McGraw-Hill Education Group",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-301-data-structures-and-algorithms-ii",
      "code": "ES 301",
      "title": "Data Structures and Algorithms II",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "300 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Anirban Dasgupta"],
      "content": "Asymptotic notation (review); Recurrences; Divide and conquer : Quick sort, Merge sort, Linear-time Median finding, Strassen's matrix multiplication, Closest Pair; Greedy and Dynamic Programming, Graph Algorithms : Network flows and applications; Introduction to complexity classes; NP-completeness reductions; Number-theoretic Algorithm; Intermediate data structures: Height balanced trees, Amortized Analysis. Optional specialized topics: String algorithms, randomized algorithms (like min-cut) other models of analysis such as streaming/online/dynamic. Linear Programming. Dealing with intractability: Approximation Algorithms, Exact Algorithms etc.",
      "textbooks": "J. Kleinberg and E. Tardos, Algorithm Design, Pearson, 1st Ed, 2013.T. Cormen, C. E. Leiserson, R. L. Rivest and C.Stein, Introduction to Algorithms, PHI Learning Private Limited-New Delhi; 3rd edition (2010)S. S. Skiena, The Algorithm Design Manual, Springer, 1998.R. Sedgewick and W. Kevin., Algorithms, Fourth Edition. Pearson Education India, 2011",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-333-microprocessors-and-embedded-systems",
      "code": "ES 333",
      "title": "Microprocessors and Embedded Systems",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "300 level",
      "lecture": 2,
      "theory": 1,
      "practical": 2,
      "ltpLabel": "2-1-2",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": "Microprocessors and Embedded Systems",
      "textbooks": "Microprocessors and Embedded Systems",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-334-interface-design",
      "code": "ES 334",
      "title": "Interface Design",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "300 level",
      "lecture": 2,
      "theory": 0,
      "practical": 3,
      "ltpLabel": "2-0-3",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": "Interface Design",
      "textbooks": "Interface Design",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-335-machine-learning",
      "code": "ES 335",
      "title": "Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "300 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [
        "Nipun Batra",
        "Anirban Dasgupta",
        "Mayank Singh",
        "Shanm"
      ],
      "content": "Introduction to learning: Learning as a combination of representation, evaluationand optimization; applications of machine learningCategorization of learning (with at least one algorithm for each) regression, classification; supervised,unsupervised, semi-supervised, active, online, reinforcement; parametric v/s non-parametric; discriminative v/s generative.Practical machine learning: bias-variance trade-offs, feature selection, cross-validation, OverfittingEmpirical risk Learning algorithms and Optimisation: decision trees, ensemble learning, linear andlogistic regression, K-Nearest Neighbours, Kernel methods, Stochastic Gradient Descent (and itsunbiasedness), Dimensionality reduction, Unsupervised methods (matrix factorisation, KMeansclustering, Hierarchical clustering) Deep learning: motivation, Multi-layer perceptrons, convolutionalneural networks, recurrent neural networks, auto-encoders. Basics of Reinforcement Learning",
      "textbooks": "Christopher Bishop. Pattern Recognition and Machine Learning. Springer, 2006.; Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. New York, NY, USA::Springer series in statistics; 2001.; James, G., Witten, D., Hastie, T., Tibshirani, R. and Taylor, J., 2023. Statistical learning. In AnIntroduction to Statistical Learning: with Applications in Python (pp. 15-67). Cham: SpringerInternational Publishing.; Duda RO, Hart PE, Stork DG. Pattern classification. John Wiley & Sons; 2012 Nov 9.; Mitchell TM. Machine learning. 1997. Burr Ridge, IL: McGraw Hill. 1997; 45(37):870-7.; Murphy, K. Machine Learning: A Probabilistic Perspective. MIT Press; Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT press; 2016 Nov18.; Murphy, K.P., 2022. Probabilistic machine learning: Advanced topics. MIT press.; Murphy, K.P., 2022. Probabilistic machine learning: an introduction. MIT press; Prince, S.J., 2023. UNDERSTANDING DEEP LEARNING. MIT PRESS.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-336-computer-organization-and-architecture",
      "code": "ES 336",
      "title": "Computer Organization & Architecture",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "300 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Joycee Mekie", "Sameer Kulkarni", "Rajat Moona"],
      "content": "Introduction to Computer Organization; Introduction to Instruction Set Architecture (ISA); RISC vs. CISC; Performance Metrics; Instruction Representation in Computers; Addressing Modes; Computer Arithmetic; Introduction to Assembly Programming; Processor Architecture; Pipelining Basics, Data and Control Hazards; Exception Handling; Cache Basics; Cache Hierarchies; Cache Coherence; Virtual Memory; Memory Hierarchy; Multiprocessor Systems; SMT (Simultaneous Multithreading); SIMD/MIMD (Single/Multiple Instruction Multiple Data), Storage and I/O; Disks and Flash Memory; Performance Benchmarking; Warehouse Scale Computing.",
      "textbooks": "1. Patterson, D.A. and Hennessy, J.L., Computer Organization and Design: The Hardware/Software Interface, 5th edition, Morgan Kaufmann, 2014. (Primary) 2. Stallings, W., Computer Organization and Architecture: Designing for Performance, 10th edition, Pearson, 2014 (Secondary)3. Patterson, D.A., Computer Architecture: A Quantitative Approach, 5th edition, Morgan Kaufmann, 2011. (Reference) 4. Bryant, R.E. and O'Hallaron, D.A., Computer Systems: A Programmer's Perspective, 3rd edition, Pearson, 2015. (Reference)",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-339-integrated-circuit-fabrication-lab",
      "code": "ES 339",
      "title": "Integrated Circuit Fabrication Lab",
      "credits": [2],
      "creditLabel": "2",
      "discipline": "Integrated Circuit Design and Technology",
      "program": "Integrated Circuit Design and Technology",
      "courseType": "Others",
      "level": "300 level",
      "lecture": 0,
      "theory": 0,
      "practical": 3,
      "ltpLabel": "0-0-3",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Sandip Lashkare", "Anagh Bhaumik", "Nihar Mohapatra"],
      "content": "Overview of cleanroom environment and classifications, Cleanroom utilities and infrastructure requirements, Standard Operating Procedures (SOPs), Safety protocols and best practices, Gowning procedures, Laboratory documentation and in-lab record keeping; Microfabrication process experiments: Wafer preparation (cleaning steps and native oxide removal), Mask preparation using laser writer (Mask design), Photolithography (Spin coating, Pre-bake, UV exposure, Post-bake, Development), Physical vapor deposition (sputtering, evaporation) and process parameter optimization, Chemical vapor deposition and process parameter tuning, Wet etching (etch rate determination and testing etching profiles on various orientations of Silicon), determination of over etching, under etching and end point parameters, thermal oxidation (validation of Deal-Grove model and diffusion) and doping (pre-deposition and subsequent drive- in of impurities to form electrical junctions). Fabrication and testing of basic semiconductor devices: resistor, capacitor, diode and MOS capacitor (MOSCAP).",
      "textbooks": "1. James D. Plummer and Peter B. Graffin, Integrated Circuit Fabrication: Science and Technology, Cambridge UniversityPress, 2024.2. Stephen A. Campbell, Fabrication Engineering, Oxford University Press, 4th Edition, 2013.3. Peter Van Zant, Microchip Fabrication: A Practical Guide to Semiconductor Processing, McGraw-Hill ProfessionalPublication, 6th Edition, 2014.4. Materials Science and Engineering: An Introduction (10th Edition or latest), W. D. Callister, D. G. Rethwisch, John Wiley &Sons, 2018.5. Thin-film deposition: Principles and practice, Donald L. Smith,, McGrawHill, 1995.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-413-deep-learning",
      "code": "ES 413",
      "title": "Deep Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [
        "Anirban Dasgupta",
        "Mayank Singh",
        "Nipun Batra",
        "Krish"
      ],
      "content": "Fundamentals of Deep Learning: Mathematics for Deep Learning - Calculus, Matrices, and Probability. Classification and Regression Problems. Multi-Layer Perceptron (MLP), Deep MLP and Backpropagation Algorithm. Implementation - Training, Validation, and Testing using Deep MLP. Optimization Techniques for Deep Networks - Gradient Descent, Stochastic Gradient Descent(SGD), Momentum, Adaptive Gradient (Adagrad), Adaptive Momentum (AdaM), Root Mean Square Propagation (RMSProp). Regularization Techniques for Deep Networks - L1/L2, Data Augmentation, Dropout, DropBlock, Batch Normalization.Deep Neural Networks: Convolutional Neural Networks (CNN), Selected CNN Architectures and their Applications - LENet-5, VGG-16, AlexNet, GoogLENet, InceptionNet, ResNet. Recurrent Neural Networks (RNN), Selected RNN Architectures and their Applications - Long Short Term Memory (LSTM) Networks and its variants, PixelRNN. Generative Adversarial Networks (GAN), Selected GAN architectures and their Applications - Conditional GAN, CycleGAN, WassersteinGAN, StyleGAN, StackGAN, BigGAN.Advanced Deep Learning Tools and Techniques: Design and Implementation - 3D CNN, Transfer Learning, Domain Adaptation, Autoencoders, Attention Mechanism, and Transformers. Deep Learning on Graphs - Embeddings, Graph Neural Networks, and their Applications. Self Supervised Learning. Tips and Tricks to enhance the Performance of Deep Neural Network implementations. Recent Advances.Applications: Research domains - Sustainability, Computer Vision, Natural Language Processing, Web Mining, Signal Processing, Bioinformatics, Climate Modeling.",
      "textbooks": "Glassner, Andrew. Deep Learning: A Visual Approach. United States: No Starch Press, 2021.Glassner, Andrew. Deep Learning: From Basics to Practice. Volume 1 and 2. 2021.Goodfellow, Ian, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. Deep learning. Cambridge: MIT press, 2016. https://www.deeplearningbook.org/Zhang, Aston, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into Deep Learning. 2020. https://d2l.ai/Krohn, Jon., Bassens, Aglaé., Beyleveld, Grant. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence. N.p.: Pearson Education, 2019.Nielsen, Michael A. Neural networks and deep learning. Vol. 25. San Francisco, CA: Determination press, 2015. http://neuralnetworksanddeeplearning.com/ References:Kriesel, David. A Brief Introduction to Neural Networks. Online. https://www.dkriesel.com/en/science/neural_networks Hardt, Moritz, and Benjamin Recht. Patterns, predictions, and actions: A story about machine learning. arXiv preprint arXiv:2102.05242 (2021). https://mlstory.org/Hamilton, William L. Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 14, no. 3 (2020): 1-159. https://www.cs.mcgill.ca/~wlh/grl_book/ Stevens, Eli, Luca Antiga, and Thomas Viehmann. Deep learning with PyTorch. Manning Publications Company, 2020. https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdfRoberts, Daniel A., Sho Yaida, Boris Hanin. The Principles of Deep Learning Theory. Cambridge University Press, 2021. https://arxiv.org/abs/2106.10165v1 Raman Arora et al. Theory of Deep Learning. Draft 2021. http://simonshaoleidu.com/teaching/cs599tdl/DLbook.pdf",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-417-5g-and-beyond-an-introduction",
      "code": "ES 417",
      "title": "5G and Beyond: An Introduction",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Ravi Hegde", "Sameer G Kulkarni"],
      "content": "Evolution, standardization, application potential in IoT,AR/VR, Smart infra and Autonomous Vehicles. Core, RAN and air interface, protocol stack andlayers; 5G physical layer, air interface and RAN: Millimeter wave propagation characteristics,Modulation and demodulation. multiplexing and multiple access. OFDM, OFDMA. Multiple antennatechniques, Air interface protocol stack, frequency and time domain structures, numerologies. radiointerface and access protocol; 5G virtualization: Software defined networks, Architecture andPrinciples: Control and Data Planes, separation of control and data, SDN use cases. Networkvirtualization, NFV, service management and orchestration, Multi-access Edge computing in 5Gand B5G networks: MEC Architecture, Mobility, service continuity, traffic routing; 5G Automation:Network Slicing, Requirements for slice automation and 5G packet core services. End-2-Endnetwork slicing and service Assurances. Cloud RAN Architecture, challenges and Non-public 5Gnetworks.",
      "textbooks": "Christopher Cox, An Introduction to 5G The New Radio, 5G Network and Beyond, wiley 2020.google books link; Sahasan Ahmadi, 5G NR Architecture, Technology, Implementation, and Operation of 3GPPNew Radio Standards. Hardback ISBN: 9780081022672, eBook ISBN: 9780128134023; Peterson, Cascone, O'Connor, Vachuska, and Davie, Software-Defined Networks: A SystemsApproach 2022. Online Link",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-422-semiconductor-foundry-engineering",
      "code": "ES 422",
      "title": "Semiconductor Foundry Engineering",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Integrated Circuit Design and Technology",
      "program": "Integrated Circuit Design and Technology",
      "courseType": "Others",
      "level": "400 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["ICDT faculty"],
      "content": "Business Imperatives: CMOS Technology motivation & impact, the foundry ecosystem, value chain, Node Power/Performance/Area requirements; customer engagement (timeline, Ground Rule Validation and Design Rule Validation, Process Design Kit, Intellectual Property, Electronic Design Automation); Product Types (SOI, embedded processors, mobile application processors, servers, r.f.), Design Technology Co-Optimization, Cost Model; Fab Imperatives: Scaling roadblocks/remedies, process defect detection/classification/genesis, Automatic Process Control, stress engineering for enhanced performance, in-line metrology; Scribe Line Monitors, Risk Management (Failure modes and effects analysis), physical failure and root-cause analysis, Pareto/Yield Analysis, Statistical Process Control, device performance and reliability concerns including (Gate Oxide Integrity, Channel Hot Carriers, Gate Induced Diode Leakage, Latch-up, Electromigration, Radiation Hardening), multi-product engineering for a wide customer base; case studies for several sub-topics; Overall Imperatives: Supply Chain Management (supplier qualification, second source development, proactive stock-level monitoring), Environmental Impact Mitigation (recycling, local sourcing, carbon footprint, compliance), Final Testing (functional, parametric), Product Engineering, Continuous Improvement Programs.",
      "textbooks": "(1) Xiao, Hong, Semiconductor Manufacturing Technology, 2nd Edition, SPIE Press, 2012 (621.38152) - Access provided byLam Research Corp. @ Introduction to Semiconductor Manufacturing Technology, Second Edition orhttps://www.spiedigitallibrary.org/ebooks/PM/Introduction-to-Semiconductor-Manufacturing-Technology-Second-Edition/eISBN-9780819490933/10.1117/3.924283(2) Schroder, Dieter K., Semiconductor Material and Device Characterization, 3rd Edition, Wiley Inter-Science, 2006 (621.38152) (3) Kumar, Rakesh, Fabless Semiconductor Implementation, Mc Graw Hill, 2008 (621.3815)(4) Nenni, Daniel and Paul McLellan, Fabless: The Transformation of the Semiconductor Industry, Independent Publisher, 2014 @ Fabless 2019 Version PDF.pdf or https://semiwiki.com/books/Fabless%202019%20Version%20PDF.pdf",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-645-optimization-methods-for-machine-learning",
      "code": "ES 645",
      "title": "Optimization Methods for Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Dinesh Garg"],
      "content": "Optimization Methods for Machine Learning",
      "textbooks": "Optimization Methods for Machine Learning",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-647-pattern-recognition-and-machine-learning",
      "code": "ES 647",
      "title": "Pattern Recognition And Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Dinesh Garg"],
      "content": "Pattern Recognition and Machine Learning",
      "textbooks": "Pattern Recognition and Machine Learning",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-654-machine-learning",
      "code": "ES 654",
      "title": "Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": [],
      "content": "Machine Learning",
      "textbooks": "Machine Learning",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-659-computer-graphics",
      "code": "ES 659",
      "title": "Computer Graphics",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Shanmuganathan Raman"],
      "content": "Fundamentals: Review of Linear Algebra and Calculus, Projective Geometry - Coordinates and Transformations.Techniques: Graphics Pipeline, Rendering, Ray Casting, Ray Tracing, Shading and Shaders, Sampling, Anti-aliasing, Animation - Particle Systems and Collision Detection, Illumination, Rasterization, Color, Visual Perception, Tone Reproduction. Recent Advances. Tools - WebGL, Three.js, and OpenGL.Shape Analysis: Differential Geometry - Curves, Surfaces, Curvature, Geodesics, Manifolds and Embeddings, Geometry Processing - Point Cloud, Mesh, Laplacian Operator, Vector Fields, Optimization on Manifolds, Functional Maps, Optimal Transport. Recent Advances. Tools - Blender, MeshLab, MATLAB, and Python.Selected Applications: Shape Restoration, Shape Correspondence, Symmetry Detection, Shape Completion, Shape Segmentation, Shape Manipulation and Morphing, Cultural Heritage.",
      "textbooks": "Hughes, John F., Andries Van Dam, James D. Foley, Morgan McGuire, David F. Sklar, Steven K. Feiner, and Kurt Akeley. Computer Graphics: Principles and Practice. Fourth Edition. Pearson Education, 2014.Marschner, Steve, and Peter Shirley. Fundamentals of Computer Graphics. Fifth Edition. CRC Press, 2021.Akenine-Moller, Tomas, Eric Haines, and Naty Hoffman. Real-Time Rendering. Fourth Edition. AK Peters/CRC Press, 2019.Crane, Keenan. Discrete Differential Geometry: An Applied Introduction. 2021. Available at https://www.cs.cmu.edu/~kmcrane/Projects/DDG/ and http://www.cs.cmu.edu/~kmcrane/Projects/DDG/paper.pdf Glassner, Andrew S., ed. Graphics gems. Elsevier, 2013.Reference Books:Gortler, Steven J. Foundations of 3D Computer Graphics. MIT Press, 2012.David J Eck. Introduction to Computer Graphics. 2018. Available at https://math.hws.edu/graphicsbook/ Laga, Hamid, Yulan Guo, Hedi Tabia, Robert B. Fisher, and Mohammed Bennamoun. 3D Shape Analysis: Fundamentals, Theory, and Applications. John Wiley & Sons, 2018.Solomon, Justin. Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics. CRC Press, 2015.Botsch, Mario, Leif Kobbelt, Mark Pauly, Pierre Alliez, and Bruno Lévy. Polygon Mesh Processing. CRC Press, 2010. Pharr, Matt, Wenzel Jakob, and Greg Humphreys. Physically based Rendering: From Theory to Implementation. Fourth Edition. Morgan Kaufmann, 2021.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-661-probabilistic-machine-learning",
      "code": "ES 661",
      "title": "Probabilistic Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Anirban Dasgupta and Nipun Batra"],
      "content": "Probability refresher: Probability theory, discrete distributions, continuous distributions, joint probability distributions, sampling from different distributions (e.g. using Box-Muller transform), uncertainty modelling, information theoretic concepts: (KL-divergence, entropy); Bayesian concept learning: Likelihood, Prior, Posterior, Maximum Likelihood Estimate (MLE), Maximum A-Posteriori estimation (MAP), Full Bayesian Estimation with Conjugate Priors (Beta-Bernoulli model for the coin toss example, Normal-Normal model for estimating parameters of Normal distribution); Bayesian supervised methods: Regression: Bayesian linear regression, Robust linear regression via alternative likelihood (e.g. Laplace, Student-T); Classification: Bayesian Logistic regression, Bayesian Naive Bayes; Latent Variable modelling: Gaussian Mixture Model, Probabilistic principal component analysis (PPCA); Approximate Inference: Sampling based strategies: Rejection sampling, Importance sampling, Markov Chain Monte Carlo (MCMC), Metropolis Hastings, Gibbs sampling, No U-Turn sampler (NUTS); Variational inference: Mean field approach, Evidence Lower Bound (ELBO), Reparameterization trick, Stochastic Variational Inference, Automatic Differentiation Variational Inference (ADVI); Gaussian Processes (GP): Multivariate Normal distribution and its properties, Kernels, GP regression, GP classification, Approximate Inference and Sparse GPs; Bayesian Optimization (BO) and Active Learning (AL): AL - Query by committee, Uncertainty sampling, Expected model change, BO - Acquisition functions, GP based BO, Random Forests based BO; Probabilistic Deep Learning: MC Dropout, Deep Ensembles, Bayesian neural networks (BNNs), Deep GPs.",
      "textbooks": "1. Kevin Murphy. Machine Learning, A Probabilistic Perspective. The MIT Press, 2012. 2. Kevin Murphy. Probabilistic Machine Learning: An Introduction. The MIT Press, 20223. Kevin Murphy. Probabilistic Machine Learning: Advanced Topics. The MIT Press, 2023.4. Chris Bishop. Pattern Recognition and Machine Learning. 5. Allen Downey. Think Bayes: Bayesian Statistics in Python. Green Tea Apress, 20126. David Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 20127. Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 20068. Richard McLearth. Statistical Rethinking. CRC Press, 2020",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-667-deep-learning",
      "code": "ES 667",
      "title": "Deep Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Anirban Dasgupta", "Mayank Singh", "Nipun Batra"],
      "content": "Mathematics for Deep Learning - Calculus, Matrices, and Probability. Classification and Regression Problems. Basics of Deep Learning: Multi-Layer Perceptron (MLP), Universal Approximation Theorem, Deep MLP and Backpropagation Algorithm. Implementation - Training, Validation, and Testing using MLP. Optimisation Techniques for Deep Networks - Gradient Descent, Stochastic Gradient Descent(SGD), Momentum, Adaptive Gradient (Adagrad), Adaptive Momentum (AdaM), Root Mean Square Propagation (RMSProp). Regularisation Techniques for Deep Networks - L1/L2, Data Augmentation, Dropout, DropBlock, Batch Normalization. Convolutional Neural Networks: Convolutional Neural Networks (CNN), Selected CNN Architectures and their Applications - LENet-5, VGG-16, AlexNet, GoogLENet, InceptionNet, ResNet, and related CNNs. Recent Advances.Generative AI: Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) Networks and variants. Generative Adversarial Networks (GAN), Selected GAN architectures and their Applications. Diffusion Models and their Applications. Variational Autoencoders. Attention Mechanism and Transformers with Applications. Normalising Flows. Recent Advances.Advanced Deep Learning Tools and Techniques: Design and Implementation - 3D CNN, Transfer Learning, Domain Adaptation, Neural Tangent Kernel. Deep Learning on Graphs - Embeddings, Graph Neural Networks, and their Applications. Self-Supervised Learning. Uncertainty Quantification in Deep Learning. Tips and Tricks to enhance the Performance of Deep Neural Network implementations. Recent Advances.Applications: Research domains - Sustainability, Computer Vision, Natural Language Processing, Web Mining, Signal Processing, Bioinformatics, Climate Modeling, and Others",
      "textbooks": "Bishop, Christopher M., and Hugh Bishop. Deep Learning: Foundations and Concepts. Springer, 2024. Deep Learning - Foundations and Concepts; Prince, Simon JD. Understanding Deep Learning. MIT Press, 2023. Understanding Deep Learning; Glassner, Andrew. Deep Learning: A Visual Approach. United States: No Starch Press, 2021.; Roberts, Daniel A., Sho Yaida, and Boris Hanin. The Principles of Deep Learning Theory. Cambridge University Press, 2022. The Principles of Deep Learning Theory; Goodfellow, Ian, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. Deep learning. Cambridge: MIT Press, 2016. Deep Learning; Zhang, Aston, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into Deep Learning. 2020. Dive into Deep Learning; Simone Scardapane. Alice's Adventures in a Differentiable Wonderland. 2024. Book: Alice's Adventures in a Differentiable Wonderland - Simone Scardapane; Nielsen, Michael A. Neural networks and deep learning. Vol. 25. San Francisco, CA: Determination press, 2015. Neural networks and deep learning",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-669-advanced-computer-architecture-and-systems",
      "code": "ES 669",
      "title": "Advanced Computer Architecture and Systems",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "3-0-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Sameer Kulkarni", "Joycee Mekie", "Rajat Moona"],
      "content": "al context and evolution. Performance metrics and measurements, benchmarking techniques, Amdhal's law, Gustafson-Barsis law, Little's law and their implication. Instruction set Architecture, MIPS/RISC-V and CISC. Parallel Architecture: Instruction Level Parallelism: pipelined processor design, superscalar and out-of-order execution techniques. Dynamic scheduling, register renaming, scoreboarding, Tumasulo's algorithm, speculative execution. Multi-core and many core systems, Data level and thread level parallelism.Memory Hierarchy: Cache and Memory hierarchy, advanced techniques for cache optimization. Distributed Shared memory, Coherence protocols (MSI, MESI, MOSI). Memory Consistency models, Sequential Consistency and relaxed consistency models. Advanced cache and memory bandwidth control and monitoring using allocation technologies, RDT.GPU Architecture and Programming: Massively parallel processors, SIMT model, synchronous and asynchronous memory updates, unified virtual addressing and unified memory. Fundamentals of CUDA programming.Advanced topics on Heterogeneous and Emerging Architecture: Interconnection networks, Network-on-chip, Accelerators: near, in-memory and neuromorphic computing, NPU, DPU and TPUs. Approximate computing, intermittent and energy-harvesting computation.",
      "textbooks": "Text Books:; J.L. Hennessy, D. A. Patterson, Computer Architecture: a qualitative approach 5th Edition, Pearson Publishers, 2012.; William Stallings, Computer Organization and Architecture: 10th Edition, Pearson Publishers, 2019.Additional Reference Books:; Kai Hwang and Faye Briggs, Compute Architecture and Parallel Processing, 1st Edition, McGraw-Hill, 1984.; Ian Foster and Dennis Gannon, Cloud Computing for Science and Engineering, MIT Press, 2017. ISBN: 9780262037242",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "es-678-machine-learning",
      "code": "ES 678",
      "title": "Machine Learning",
      "credits": [4],
      "creditLabel": "4",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "600 level",
      "lecture": 3,
      "theory": 1,
      "practical": 0,
      "ltpLabel": "3-1-0",
      "semester": "Full Semester",
      "semesters": ["Full Semester"],
      "prerequisites": [],
      "instructors": ["Anirban Dasgupta", "Krishna P Miyapuram", "Manisha Pad"],
      "content": "Learning Foundations: Introduction and applications, learning paradigms, bias-variance tradeoff, empirical risk minimization, generalization, overfitting; Supervised Learning: linear and logistic regression, SVMs, decision trees, ensemble methods; Optimization: gradient descent, stochastic gradient descent, convexity, constrained optimization; Unsupervised Learning: PCA, kMeans, NMF, hierarchical clustering; Deep Learning: MLPs, CNNs, RNNs, autoencoders, autograd; Reinforcement Learning: conceptual introduction, Q-learning, value/policy intuition; Additional topics for PG students: PAC learning, Inductive bias, Deriving loss functions via Maximum Likelihood Estimation, convergence analysis and adaptive optimization, Gaussian mixture models, selfsupervised pretraining.",
      "textbooks": "Primary textbooks1. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.2. Mitchell, T. M. (1997). Machine learning. McGraw-Hill.3. Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.4. James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statisticallearning: With applications in Python (pp. 15-67). Springer International Publishing.Additional References1. Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification (2nd ed.). John Wiley & Sons.2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press3. Murphy, K. P. (2022a). Probabilistic machine learning: An introduction. MIT Press.4. Murphy, K. P. (2022b). Probabilistic machine learning: Advanced topics. MIT Press.5. Prince, S. J. D. (2023). Understanding deep learning. MIT Press.",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "sc-140-introduction-to-computer-networking",
      "code": "SC 140",
      "title": "Introduction to Computer Networking",
      "credits": [1],
      "creditLabel": "1",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "100 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Half Semester",
      "semesters": ["Half Semester"],
      "prerequisites": [],
      "instructors": ["Prof. Tannistha Samanta"],
      "content": "Introduction To Computer Networking",
      "textbooks": "Introduction To Computer Networking",
      "status": "active",
      "sourceRowCount": 1
    },
    {
      "id": "sc-240-foundations-of-state-estimation",
      "code": "SC 240",
      "title": "Foundations of State Estimation",
      "credits": [1],
      "creditLabel": "1",
      "discipline": "Comp Science & Engg",
      "program": "Computer Science and Engineering",
      "courseType": "Others",
      "level": "200 level",
      "lecture": 0,
      "theory": 0,
      "practical": 0,
      "ltpLabel": "0-0-0",
      "semester": "Half Semester",
      "semesters": ["Half Semester"],
      "prerequisites": [],
      "instructors": ["Dr. Bala Natarajan"],
      "content": "Introduction to inferencing: Statistical models, likelihood function, Fisher information, Unbiasedness, Sufficiency conditions, Hypothesis testing basics: Assessing evidence, Decision Rules, parametric and nonparametric testsBayesian parameter estimation: Bayes Theorem, Mean and variance estimation, independence of parameters, Minimum variance unbiased estimators: Theory of point statistical estimation, Cramer-Rao Lower Bound, Techniques for improving estimatorsMaximum likelihood estimation and EM algorithm: Detailed description of expectation maximization and maximum likelihood algorithm, BLUE, Wiener filters: Classical Wiener filter problem, introduction to least-mean square adaptive filters, Wiener filter in detailKalman filters: Idea of Recursion, Linear filter based on dynamic models, statistical properties of Kalman filters, Hidden Markov models and Baum welch algorithm: Independent Mixture models, Markov Chain, Introduction to Hidden Markov Models (HMM), Estimation of parameters in HMM by various approachesState estimation in Smart Grid, Applications: Biomedical systems",
      "textbooks": "The objective of this short course is to introduce the theoretical foundations of state estimation. Specifically, we will consider the following general framework: given a set of observations, we make inferences about some underlying phenomenon of interest. This will include choosing between a fixed set of possible scenarios (hypothesis) as well as estimating a particular parameter of interest. In both cases, we will try to optimize some form of cost function. With the focus on the latter problem (parameter estimation), this course will equip the students with the theoretical foundations necessary to address numerous practical problems that span all areas of engineering, computer science and even management.",
      "status": "active",
      "sourceRowCount": 1
    }
  ]
}
