Course Details
| Course Code | Course Name | Target audience | Course contents | Suggested references | Core or Elective |
|---|---|---|---|---|---|
| AI 5030 | Probability and Stochastic Processes | M.Tech and PhD | Probability on finite sample spaces; joint and conditional probabilities; independence; total probability; Bayes rule; random variables; CDF/PMF/PDF; discrete and continuous distributions; mean and variance; central limit theorem; Markov and Chebyshev inequalities; Chernoff bounds; estimation; random processes; Markov chains; Markov decision processes; Poisson point processes. | Sheldon Ross, Introduction to Probability Models, Academic Press, 2019; Athanasios Papoulis and S. Unnikrishna Pillai, Probability, Random Variables, and Stochastic Processes, Tata McGraw-Hill, 2002; Dimitri P. Bertsekas and John N. Tsitsiklis, Introduction to Probability, Athena Scientific, 2007. | Core |
| AI 5000 | Machine Learning | MTech (1st year), PhD (1st year) | ML & Learning Paradigms: ML, Types of ML, Probability Refresher Foundations & Methodology: Bias-Variance Tradeoff, Generalization Error, Cross-Validation, and Regularization techniques. Linear Models (Regression): Maximum-likelihood estimate (MLE), Maximum a posteriori (MAP) estimate, Least squares estimate, Geometric Interpretation of Least Squares Linear Models (Classification): Linear discriminant functions, Fisher discriminant analysis, Perceptron, Support Vector Machines (SVM), Bayes decision theory, generative vs. discriminative models (ML and MAP approaches), and Logistic Regression. Nonlinear Models: Cover's theorem, Nonlinear kernel transformation (fixed vs. data-driven kernels), Decision Trees, Ensemble Methods, k-Nearest Neighbors, Feed-forward neural networks, error back-propagation Unsupervised Learning: Clustering, density estimation, Gaussian mixture models (GMM), dimensionality reduction (e.g., PCA), and autoencoders with variants. | 1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. 2. Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, 2014 3. Murphy K. P., 2022, Probabilistic Machine Learning: An Introduction | Murphy | Core |
| AI 5100 | Deep Learning | MTech(1st year), PhD(1st year) | Foundations of Deep Learning: Artificial Neuron Models (MP neuron, Perceptron), Perceptron Convergence, Network of Perceptrons, and the Representational Power of Multi-Layer Perceptrons (MLPs). Training Deep Neural Networks: Gradient Descent, Backpropagation, Cross-Entropy Loss, Optimization algorithms, and Regularization techniques. CNNs: Building blocks of Convolutional Neural Networks (CNNs), Evolution of CNN Architectures, and Visualizing/Understanding CNNs (CAM, Grad-CAM, DeepDream, Style Transfer). Sequence Modeling & NLP: Recurrent Neural Networks (RNNs, LSTMs, GRUs) and Word Embeddings(GloVe, Word2Vec). Attention & Transformers: Encoder-Decoder Models, Attention Mechanisms, Self-Attention, Transformer, and their downstream applications(GPT, BERT, LLM). Generative Models: Autoencoders, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models. Contrastive Learning: SimCLR,CLIP, SigLIP Multimodal Large Language Models: Llava, Qwen, GLM | 1. Bishop, C. M., & Bishop, H. (2023). Deep learning: Foundations and concepts. Springer 2. Goodfellow et al., Deep Learning (2016), MIT Press | Core |
| AI 5110 | Linear Algebra and Applications | M.Tech, PhD | Vector spaces; inner products and distances; linear transformations; systems of linear equations; linear dependence/independence; column and null spaces; rank, dimension and rank-nullity; orthogonal matrices; projections; Gram-Schmidt; QR decomposition; trace; matrix-operation complexity; inverse and pseudoinverse; eigenvalues/eigenvectors; EVD; SVD; Jordan canonical form; least squares and variants; PCA; low-rank approximations; Eckart-Young-Mirsky theorem; randomized matrix multiplication, JL lemma and randomized least squares. | Boyd and Vandenberghe, Introduction to Applied Linear Algebra, Cambridge, 2018; Horn and Johnson, Matrix Analysis, Cambridge, 2012; Sheldon Axler, Linear Algebra Done Right, Springer, 1997; Gilbert Strang, Linear Algebra and Learning from Data, Wellesley-Cambridge, 2019. | Core |
| AI 5120 | Topics in Optimization | MTech, PhD, and third or final-year BTech. | Convex Sets, Convex Functions, Operations that preserve convexity, Convex Optimization Problems, Duality. Algorithms: Unconstrained Minimization, Equality Constrained Minimization, Interior Points Methods. Applications: Support Vector Machines, Compressed Sensing, Linear and Logistic Regression, Nuclear Norm Minimization Semidefinite Programming (SDP), Low-Rank Matrix Approximation. | 1. S. Boyd L. Vandenberghe. Convex Optimization, Cambridge. 2. D. Bertsekas. Convex Optimization Theory, Athena Scientific. | Core |
| AI 5040 | Game Theory and Mechanism Design | M.Tech, PhD, B.Tech IV | Cooperative and non-cooperative games; strategic-form games; matrix games; dominant-strategy equilibria; Nash equilibria; Shapley value; correlated equilibria; mechanism design; Gibbard-Satterthwaite theorem; auctions; matching; voting; cake division. | Y. Narahari, Game Theory and Mechanism Design, 2016, 1st ed.; Michael Maschler, Eilon Solan and Shmuel Zamir, Game Theory, 2013, 2nd ed.; Nisan, Roughgarden, Tardos and Vazirani, Algorithmic Game Theory, Cambridge University, 2007. | Elective |
| AI 5073 | Neuromorphic Artificial Intelligence | M.Tech, PhD, B.Tech IV | Brain computation at microcircuit, system and behavioral levels; neuron models including LIF and Hodgkin-Huxley; sensory coding in vision, audition, smell and touch; brain-inspired networks; attractors; vector symbolic architectures; spiking neural networks; continual and local learning; signal processing; encoding/decoding; neuromorphic architectures; neuromorphic deep learning including surrogate gradients and DNN-to-SNN conversion. | Gerstner, Kistler, Naud and Paninski, Neuronal Dynamics, 2014, 1st ed.; Goodfellow, Bengio and Courville, Deep Learning, MIT Press, 2016; Sterling and Laughlin, Principles of Neural Design, MIT Press, 2015; Breedlove and Watson, Behavioral Neuroscience, 2017, 8th ed.; Bermudez, Cognitive Science, 2017, 2nd ed. | Elective |
| AI 5080 | Generative Artificial Intelligence (GenAI) | PhD, Masters and final-year Bachelors in AI and CSE | Generative modeling and foundation models; GANs and VAEs; transformers for vision and language; diffusion models; flow-based models; DALL-E; language modeling and LLMs including autoregressive models, GPT and Llama; pre-training, fine-tuning, RLHF, adapters and in-context learning; bias, hallucination, adversarial attacks and data contamination; multimodal models including CLIP, CLAP and ViT; evaluation of generative models; responsible AI. | Goodfellow, Bengio and Courville, Deep Learning, MIT Press, 2016; Simon J. D. Prince, Understanding Deep Learning, MIT Press, 2023; Vaswani et al., Attention Is All You Need, NeurIPS 2017; Radford et al., Improving Language Understanding by Generative Pre-Training, 2018; Radford et al., Language Models are Unsupervised Multitask Learners, 2019; Ho et al., Denoising Diffusion Probabilistic Models, NeurIPS 2020; other relevant papers. | Elective |
| AI 5090 | Stochastic Processes: Theory and Applications | B.Tech 4th year, M.Tech 2nd year, PhD all years | Sequences of random variables; convergence modes; CLT, WLLN, SLLN; Bernoulli processes; random processes and finite-dimensional distributions; stationarity; autocorrelation/cross-correlation; finite-state discrete-time Markov chains; transition matrices; Chapman-Kolmogorov equations; recurrence/transience; communicating classes; stationary distributions; stopping times; strong Markov property; Wald identity; ergodic theorem; MCMC and Metropolis; Markov-chain applications in AI/ML including Markovian bandits; Poisson processes. | R. G. Gallager, Stochastic Processes: Theory for Applications, Cambridge, 2013; Anurag Kumar, Discrete Event Stochastic Processes lecture notes, IISc; Grimmett and Stirzaker, Probability and Random Processes, Oxford, 2020; Vincent Y. F. Tan stochastic-process lectures; V. Moulos, Concentration and Sequential Decision Making in Markovian Environments, UC Berkeley, 2020; Küchler and Sørensen, On Exponential Families of Markov Processes, 1998. Source(s): [Course Proposal] AI 5090 - Stochastic-Processes and Applications.docx | Elective |
| AI 5050 | Explainability in Machine Learning | M.Tech, PhD, B.Tech IV | Interpretability and explainability of ML models; interpretable models including prototype-based approaches, sparse linear models, rule-based techniques and generalized additive models; post-hoc black-box explanations; counterfactual explanations; saliency maps; links to causality, debugging, bias and fairness; applications in criminal justice, computer vision and healthcare; student presentations and semester-long project. | Christopher Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 2023, 1st ed.; Uday Kamath and John Liu, Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning, Springer, 2021, 1st ed. | Elective |
| AI 5133 | AI and Sensors | B.Tech IV, M.Tech, PhD | Analog-to-digital converters; calibration; sensor signal processing; design principles, preprocessing and encoding; AI-based handling of data from CMOS image sensors, event-based cameras, angle-sensitive pixels, X-rays, lidars, chemical sensors, force/strain/tactile sensors, pressure/flow sensors, occupancy and motion detectors, position/displacement/level sensors, velocity and acceleration sensors, and acoustic sensors; neural networks, k-means, SVM and Kalman filtering for sensor systems. | Jacob Fraden, Handbook of Modern Sensors: Physics, Designs, and Applications, Springer, 4th ed., 2010; Gerard Meijer, Michiel Pertijs and Kofi Makinwa, Smart Sensor Systems: Emerging Technologies and Applications, IEEE Press, 2014; Goodfellow, Bengio and Courville, Deep Learning, MIT Press, 2016. | Elective |
| AI 5153 | Mobile Robotics | B.Tech IV, M.Tech, PhD from AI, ME and EE departments | Introduction to mobile robots: history, applications and robot types; mobile robot kinematics, locomotion and wheel types; holonomic and non-holonomic robots; behavior-based, subsumption, reactive and deliberative robotic paradigms; sensors and actuators; uncertainty characterization; motors and wheels; localization including odometry, Kalman/EKF, particle filters and SLAM; planning and navigation including map representation, A*, RRT and PRM; trajectory tracking, optimization, model-based and model-free control. | Peter Corke, Robotics, Vision and Control, 2nd ed., 2017; Siegwart, Nourbakhsh and Scaramuzza, Introduction to Autonomous Mobile Robots, MIT Press, 2nd ed., 2011; lecture/tutorial slides, handouts and notebooks. | Elective |
| AI 5163 | Cybersecurity and AI | B.Tech 4th year, M.Tech 1st/2nd year, PhD 1st year | Introduction to cybersecurity; AI in threat detection and vulnerability management; ethical hacking using AI systems; securing AI systems; AI and generative AI for automated response to network intrusions; AI for automated vulnerability assessments; AI in identity and access management; data privacy and AI; AI in cybersecurity governance; practical group projects. | Brooks, Cybersecurity Essentials, Wiley, 2018; Mitnick and Simon, The Art of Deception, Wiley, 2011; Roberts and Brown, Intelligence-Driven Incident Response, OReilly, 2017; Tsukerman, Machine Learning for Cybersecurity Cookbook, Packt, 2019; Gil and Liska, Security with AI and Machine Learning, OReilly, 2019; Wu, Ge and Li, AI and Machine Learning for Network and Security Management, Wiley-IEEE, 2022; Kaur et al., Artificial Intelligence for Cybersecurity, Information Fusion, 2023; Li et al., Differential Privacy, Springer, 2016; Hu et al., LLM-TIKG, 2023. | Elective |
| AI 5170 | Introduction to Stochastic Differential Equations | B.Tech III/IV, M.Tech, PhD | Modern probability review; continuous-time stochastic processes; Brownian motion; Wiener integral; construction of Brownian motion; stochastic integrals; extensions of stochastic integrals; stochastic integrals for martingales; Ito formula and applications; stochastic differential equations; filtering applications; finance applications; Feynman-Kac formula. | Gopinath Kallianpur, Stochastic Filtering Theory, Springer New York, 1980, 1st ed.; Hui-Hsiung Kuo, Introduction to Stochastic Integration, Springer New York, 2005, 1st ed.; Bernt Oksendal, Stochastic Differential Equations, Springer Berlin Heidelberg, 2003, 6th ed. | Elective |
| AI 7010 | An Overview of Reinforcement Learning | A. Background material: Introduction to probability and stochastic processes; Markov processes. B. Markov Reward and Markov Decision Processes: Value iteration; Bellman's optimality equation; policy iteration. C. Stochastic Approximation: Standard stochastic approximation and its variants, namely asynchronous and two time-scale; finite-time stochastic approximation. D. Approximation Methods for Solving MDPs: Monte Carlo simulation; temporal difference learning. E. Parametric Approximation Methods: Value approximation methods; policy gradient approach; Zap Q-learning; deep Q-learning. F. Introduction to Statistical Learning: Vapnik-Chervonenkis (VC) and Pollard (P) dimensions; applications to concept and function learning; applications to neural network learning. G. Finite-Time Reinforcement Learning: PAC-MDP; finite-time stochastic approximation | R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction (Second Edition). MIT Press, 2018. Csaba Szepesvári, Algorithms for Reinforcement Learning, Morgan & Claypool, 2010 | Elective | |
| AI 3403 | Multi-Agent Systems | B.Tech (3rd and 4th Year), M.Tech, PhD | Quick Review of Linear Algebra & Basic Optimization, Fundamentals of Graph Theory, Consensus & Resilient Distributed Control, Coordination between Agentic AI, Basic Coordination Algorithms, Distributed Learning, ADMM, Formal Methods for Multi-agent Systems: Signal Temporal Logics & Epistemic Logics. | 1) Gerhard Weiss, “Multi-Agent Systems - A Modern Approach to Distributed Artificial Intelligence”, MIT Press, 1999. 2) Francesco Bullo, “Lectures on Networked Systems”, Edition 1.7 - April 2024, ISBN 978-1-986425-64-3. 3) Lars Lindemann, and Dimos V. Dimarogonas, “Formal Methods for Multi-Agent Feedback Control Systems”, MIT Press, ISBN: 9780262382793, 2025. 4) Hans van Ditmarsch, Wiebe van der Hoek, and Barteld Kooi, “Dynamic Epistemic Logic”, Springer Publications, 2007. 5) https://stanford.edu/~boyd/admm.html | Elective |
| AI 1233 | Optimization-I | B.Tech I | Convex sets and functions (calculus of convex sets and functions, epigraphs, quasi convexity and other properties), basics of convex analysis, convex optimisation (subtypes: LP, QP, QCQP, SOCP, SDP, Geometric programs: definitions and properties; transformation techniques). Modeling real world problems as convex optimization problems, introduction to disciplined convex programming and using CVX. KKT conditions and duality, convex relaxation and examples. | Boyd, Stephen P., and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004. Bertsekas, Dimitri, Angelia Nedic, and Asuman Ozdaglar. Convex analysis and optimization. Vol. 1. Athena Scientific, 2003. | Core |
| AI 2200 | Concentration Inequalities | B.Tech II | Recap: Markov inequality, Chebyshev inequality, Jensen’s inequality. Chernoff Bounds, sub Gaussian tail bounds, Han’s inequality, Hoeffding’s inequality, Bernstein’s inequality, Effron-Stein’s inequality, transportation inequality, log-sobolev inequality, bounded difference property, coupling. Applying concentration inequalities to example problems. | Vershynin, Roman. High-dimensional probability: An introduction with applications in data science. Vol. 47. Cambridge university press, 2018. Sridharan, Karthik. "A gentle introduction to concentration inequalities." Dept. Comput. Sci., Cornell Univ., Tech. Rep (2002). Boucheron, Stéphane, Gábor Lugosi, and Olivier Bousquet. "Concentration inequalities." Summer school on machine learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. 208-240. | Core |
| AI 2111 | Optimization-II | B.Tech II | First-order methods for convexity: gradient descent and variants, analysis of gradient descent. Second-order methods. Subgradient and ellipsoid methods. Proximal methods. Applications to signal processing, machine learning and other related areas. | Beck, Amir. First-order methods in optimization. Society for Industrial and Applied Mathematics, 2017. Nesterov, Yurii. Lectures on convex optimization. Vol. 137. Berlin: Springer, 2018. Boyd, Stephen P., and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004. | Core |
| AI 3703 | Natural Language Processing | B.Tech III | Introduction to statistical NLP, Parts of Speech tagging, Named Entity Recognition, Parsing , Representation Learning and word embedding Encoder Decoder models and Transformers, Coreference Resolution, Discourse Language Modeling and Large Language Models | Speech and Language Processing 3rd Edition by Jurafsky and Martin Jurafsky, Dan. Speech & language processing. Pearson Education India, 2000. Eisenstein, Jacob. "Natural language processing." Jacob Eisenstein (2018). Goldberg, Yoav. "A primer on neural network models for natural language processing." Journal of Artificial Intelligence Research 57 (2016): 345-420. | Core |
| AI 3603 | Computer Vision | B.Tech III | Image formation (Camera types, projection, light), multi-view geometry, reconstruction, low-level image processing (convolution, filtering, effects), interest points, descriptors, matching, and high-level vision tasks like image classification, object detection, semantic segmentation, etc. Learning-based vision (CNNs, ViTs, etc.) | 1. Computer Vision: Algorithms and Applications by Richard Szeliski. 2. Computer vision: A modern approach: Forsyth and Ponce, Pearson. | Core |
| AI 1010 | Intro to Classical AI | B.Tech I | Search strategies : Breadth first search, depth first search, best first search , Games and adversarial search, A* algorithm, heuristic search, minimax algorithm, alpha-beta pruning, constraint satisfaction, propositional logic, first order logic, inference in first order logic, probabilistic reasoning, Bayes rule, Bayesian networks | Norvig, P. Russel, and S. Artificial Intelligence. A modern approach. Prentice Hall, 2002. Kevin Knight, Elaine Rich, Shivashankar B. Nair, ARTIFICIAL INTELLIGENCE Third Edition McGraw Hill, 2017 | Core |
| AI 1013 | Programming for AI | B.Tech I | Data types, functions, loops, conditional statements, tuples, lists, dictionaries, Pandas, NumPy, SciPy, Matplotlib, Seaborn. Data analysis in Python using real-world datasets. Learning to implement core tasks in AI/ML such as training of artificial neural networks, backpropagation, k-means clustering, and k-nearest neighbor classification from scratch. Course will behave a strong Lab and project component. Assignments and exercises will help students gain familiarity with Python programming and basic linux commands, and will be tailored towards implementing advanced algorithms in Deep Learning and Reinforcement Learning in subsequent semesters. | Bader, Dan; Jablonski, Joanna; Amos, David; Heisler, Fletcher. Python Basics: A Practical Introduction to Python 3 Joshi, Prateek. Artificial intelligence with python. Packt Publishing Ltd, 2017. Bratko, I. Prolog Programming for Artificial Intelligence. Addison Wesley. (2000). Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, 2019. | Core |
| AI 1001 | Introduction to Modern AI | B.Tech I | This course provides a high level overview of the foundational principles that drive AI-based technologies. Topics to be introduced include - concepts of modelling, inference and learning; Linear and nonlinear models, supervised and unsupervised learning, classification, regression and multi layer perceptions, SVMs; state based models, Markov decision processes, games, Bayesian networks; constraint satisfaction and logic; example applications. | Core | |
| AI 4000 | Robotics | B.Tech IV | Mathematical modeling of robots, rigid motion in 3D, forward and inverse kinematics, dynamic equations of motion, motion planning, joint control, feedback linearization, underactuation, mobile robots. | 1) Robot Modeling and Control by Spong, Hutchinson, and Vidyasagar, second edition, Wiley, 2020. 2) Introduction to Robotics by S K Saha, second edition, McGraw Hill Education, 2017. | Core |
| AI 3000 | Reinforcement Learning | B.Tech III | Introduction and motivation to reinforcement learning; Markov decision process; Dynamic programming and Bellman optimality principle; Model based techniques : Value and policy iteration; Model free approaches : Q-learning and SARSA; Function approximation; Introduction to Deep RL, Deep Q Networks (DQN); Policy gradient techniques; Actor-critic Methods (Asynchronous (A3C) and synchronous (A2C) methods); Deterministic policy gradients; Natural policy gradient based algorithms. | 1) Andrew Barto and Richard Sutton, Reinforcement Learning : An Introduction, MIT Press, 1998. 2) Dimitri Bertsekas, Reinforcement Learning and Optimal Control, Athena Scientific, 2019 3) Csaba Szepesvari, Algorithms for Reinforcement Learning, Morgan and Claypool, 2010 | Core |
| AI 2000 | Foundations of Machine Learning | B.Tech II | Introduction to Machine Learning; Classification methods: k-NN, Naive Bayes classifier, Decision Trees, Support Vector Machines, Kernel Methods, Neural Networks, Ensemble Methods: Bagging, Boosting, Adaboost, Random Forests; Regression methods: Linear regression, Generalized Linear Models, Logistic Regression, Kernel Ridge regression, Support Vector Regression; Clustering methods: k-means, Gaussian Mixture Models, Expectation Maximization, Hierarchical clustering, Spectral clustering; Dimensionality reduction methods: Principal component analysis, Independent component analysis, Manifold learning, Discriminant analysis | Main references: 1. Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer, 2006. ISBN 978-0-387-31073-2 2. Alpaydin, Ethem Introduction to Machine Learning. MIT Press, 2014. ISBN 978-0-262-02818-9 Other useful references: 1. Duda, Richard, Peter Hart, and David Stork. Pattern Classification. 2nd ed. New York, NY: Wiley-Interscience, 2000. ISBN: 9780471056690. 2. Mitchell, Tom. Machine Learning. New York, NY: McGraw-Hill, 1997. ISBN: 9780070428072. 3. Bishop, Christopher. Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, 1995. ISBN: 9780198538646. 4. Hastie, T., R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York, NY: Springer, 2001. ISBN: 9780387952840. 5. MacKay, David. Information Theory, Inference, and Learning Algorithms. Cambridge, UK: Cambridge University Press, 2003. ISBN: 9780521642989. Available on-line. | Core |
| AI 2100 | Foundations of Machine Learning | B.Tech II | Introduction/History of Neural Networks, Feedforward neural networks, Backpropagation, Regularization methods, Optimization methods, Convolutional Neural Networks (CNNs), Visualizing and Understanding CNNs, Sequence learning with NNs, Recurrent Neural Networks, LSTM and GRU, Deep Unsupervised Learning, Autoencoders, Deep Generative Models, Variational Autoencoders, Generative Adversarial Networks (GANs), Deep Reinforcement Learning. | Books/References 1. Deep Learning By Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016 2. Neural Networks and Deep Learning, By Michael Nielsen, Online book, 2016 3. Learning Deep Architectures for AI (slightly dated) By Yoshua Bengio, NOW Publishers, 2009 | Core |
| AI 4013 | AI for Humanity | B.Tech IV | 1. Different notions of fairness; disparate impact, group based and individual fairness. 2. Bias and Fairness in AI with applications in classification, regression, clustering, ranking and bandits; algorithms and tools, 3. Data analysis; pre, in and post-processing techniques to remove bias in data 4. Differential privacy, security, causality issues and AI algorithms | Books/References Books: Fairness and Machine Learning, limitations and Opportunities by Solon Barocas, Moritz Hardt, Aravind Narayanan (not published yet) online version available here Weapons of Math Destruction, How Big Data Increases Inequality and Threatens Democracy, by Cathy O’Neil, 2016, Broadway books, First Edition The Ethical Algorithm: The science of socially aware algorithm design, by Michael Kearns and Aaron Roth, Oxford University Press, 2019, First Edition 4. Algorithms of Oppression: How Search Engines Reinforce Racism, by Safiya Umoja Noble, 2018, NYU Press, First Edition . 5. Automating Inequality:How high-tech tools profile, police, and punish the poor by Virginia Eubanks, 2018, St. Martin's Press, First Edition 6. Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard, 2018, MIT Press, First Edition. | Core |
| AI 4010 | Online Learning | B.Tech IV, MTech, PhD | Online Convex Optimization: Online gradient descent, exponentially weighted online convex optimization, regularization techniques, follow-the-regularized-leader, online mirror descent, randomized regularization, follow-the-perturbed-leader technque for expert advice Online Learning:Online classification, regret minimization, prediction with expert advice, minimax regret, discounted regret, follow the best expert strategy, tight regret bonds for specific class of loss functions, randomized prediction, calibration, prediction with limited feedback, bandit learning: stochatic and adversarial multi-armed bandit settings, EXP3/EXP4 algorithms and their varients, UCB1 and its varients, Thomson Sampling and contextual bandits. Applications: online spam filtering, clinical trials, game playing, sequential investment/portfolio selection, recommendation systems, universal lossless data compression. | 1. "Prediction, Learning and Games". Nicolo Cesa-Bianchi and Gabor Lugosi, Cambridge University Press, 2006 References 2. “ Bandit Algorithms.” Lattimore T, Szepesvári C. Cambridge University Press; 2020. 3. “Introduction to Online Convex Optimization”, Elad Hazan, MIT Press, 2022 | Elective |
| AI 4803 | AI in Finance | B.Tech IV, MTech, PhD | This course offers an in-depth exploration of how Artificial Intelligence and Machine Learning are transforming modern finance. It begins with a brief history of computing in finance, the evolution of trading, and an introduction to high-frequency trading (HFT). Students will learn about market microstructure, including order types, order-driven markets, and liquidity dynamics. Core topics include time-series modelling, volatility forecasting, and the application of scale-invariant patterns—such as directional changes—in financial market analysis. The course emphasizes algorithmic trading in FX markets. Students will use ML methods for portfolio optimization and trading signal generation and will build and back-test their own trading algorithms. The course also provides practical insights into deploying trading strategies in live markets and explores how AI/ML can be applied in automated trading within high-frequency financial markets. | Marcos Lopez de Prado, Advances in Financial Machine Learning, Wiley, 1st edition 2018. Edward Tsang, AI for Finance (AI for Everything), CRC Press, 1st edition, 2023. High frequency trading: a practical guide to algorithmic strategies and trading system, Irene Aldridge, 2013, John Wiley & Sons. ISBN 9781118343500 An introduction to high-frequency finance, Dacorogna et al., 2001, Academic press. ISBN 0122796713 Trading & Exchanges: Market Microstructure for Practitioners, Larry Harris, 2002, Oxford University Press. ISBN 9780198090540 | Elective |
| AI 1110 | Introduction to Probability and Random Variables | B.Tech I | Probability space, pmf, pdf, cdf, mean and variance, examples and modelling using random variables; functions of random variables, convergence of sequence of random variables, Markov and Chebyshev inequalities, Chernoff bounds; introduction to estimation (LMSE, MMSE and MLE) | Core | |
| AI 1000 | Matrix Theory | B.Tech | Systems of linear equations; row and column viewpoints; Gaussian elimination; matrix operations; inverses; vector spaces, subspaces, span, linear independence, basis, dimension, rank, and null spaces; linear transformations and matrix representations; determinants and their geometric interpretation; inner products, norms, orthogonality, projections, Gram–Schmidt orthogonalization, and least-squares problems; eigenvalues, eigenvectors, diagonalization, and spectral decomposition; symmetric, orthogonal, positive definite, and positive semidefinite matrices; singular value decomposition and low-rank approximation; Applications and carefully selected examples from modern machine learning and deep learning. | Core |