Course Contents

Course Details

Course Details
Course Code Course Name Target Audience Course Contents Suggested References
AI1000 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 (SVD) and Low-Rank Approximation; Applications and Carefully Selected Examples from Modern Machine Learning and Deep Learning.
AI1001 Introduction to Modern AI B.Tech I This course provides a high-level overview of the foundational principles that drive AI-based technologies. Topics Covered: Concepts of Modelling, Inference, and Learning; Linear and Nonlinear Models; Supervised and Unsupervised Learning; Classification; Regression; Multi-Layer Perceptrons; Support Vector Machines (SVMs); State-Based Models; Markov Decision Processes; Games; Bayesian Networks; Constraint Satisfaction; Logic; and Example Applications.
AI1010 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, and Bayesian Networks. 1. Norvig, P., & Russell, S. Artificial Intelligence: A Modern Approach. Prentice Hall, 2002. 2. Kevin Knight, Elaine Rich, and Shivashankar B. Nair. Artificial Intelligence, Third Edition, McGraw Hill, 2017.
AI1013 Programming for AI B.Tech I Data types, functions, loops, conditional statements, tuples, lists, dictionaries, Pandas, NumPy, SciPy, Matplotlib, and Seaborn. Data analysis in Python using real-world datasets. Learning to implement core AI/ML tasks such as training artificial neural networks, backpropagation, k-means clustering, and k-nearest neighbor classification from scratch. The course includes a strong laboratory and project component. Assignments and exercises help students gain familiarity with Python programming and basic Linux commands and prepare them for implementing advanced algorithms in Deep Learning and Reinforcement Learning in subsequent semesters. 1. Bader, Dan; Jablonski, Joanna; Amos, David; Heisler, Fletcher. Python Basics: A Practical Introduction to Python 3. 2. Joshi, Prateek. Artificial Intelligence with Python. Packt Publishing Ltd., 2017. 3. Bratko, I. Prolog Programming for Artificial Intelligence. Addison Wesley, 2000. 4. Géron, Aurélien. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2019.
AI1110 Introduction to Probability and Random Variables B.Tech I Probability space, probability mass function (PMF), probability density function (PDF), cumulative distribution function (CDF), mean and variance, examples and modelling using random variables, functions of random variables, convergence of sequences of random variables, Markov and Chebyshev inequalities, Chernoff bounds, and an introduction to estimation techniques including Least Mean Square Error (LMSE), Minimum Mean Square Error (MMSE), and Maximum Likelihood Estimation (MLE).
AI1233 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.
  1. Boyd, Stephen P., and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.

  2. Bertsekas, Dimitri, Angelia Nedic, and Asuman Ozdaglar. Convex analysis and optimization. Vol. 1. Athena Scientific, 2003.
AI2000 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.
AI2100 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.
  1. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
  2. Nielsen, Michael. Neural Networks and Deep Learning. Online book, 2016.
  3. Bengio, Yoshua. Learning Deep Architectures for AI. NOW Publishers, 2009.
    (Slightly dated)
AI2111 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.
  1. Beck, Amir. First-order Methods in Optimization. Society for Industrial and Applied Mathematics, 2017.
  2. Nesterov, Yurii. Lectures on Convex Optimization. Vol. 137. Berlin: Springer, 2018.
  3. Boyd, Stephen P., and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
AI2200 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.
  1. Vershynin, Roman. High-Dimensional Probability: An Introduction with Applications in Data Science. Vol. 47. Cambridge University Press, 2018.
  2. Sridharan, Karthik. A Gentle Introduction to Concentration Inequalities. Dept. Comput. Sci., Cornell Univ., Tech. Rep., 2002.
  3. Boucheron, Stéphane, Gábor Lugosi, and Olivier Bousquet. Concentration Inequalities. Summer School on Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 208–240.
AI3000 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. Barto, Andrew, and Richard Sutton. Reinforcement Learning: An Introduction. MIT Press, 1998.
  2. Bertsekas, Dimitri. Reinforcement Learning and Optimal Control. Athena Scientific, 2019.
  3. Szepesvari, Csaba. Algorithms for Reinforcement Learning. Morgan and Claypool, 2010.
AI3603 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. Szeliski, Richard. Computer Vision: Algorithms and Applications.
  2. Forsyth and Ponce. Computer Vision: A Modern Approach. Pearson.
AI3703 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.
  1. Jurafsky, Dan, and James H. Martin. Speech and Language Processing. 3rd Edition.
  2. Jurafsky, Dan. Speech & Language Processing. Pearson Education India, 2000.
  3. Eisenstein, Jacob. Natural Language Processing. 2018.
  4. Goldberg, Yoav. A Primer on Neural Network Models for Natural Language Processing. Journal of Artificial Intelligence Research, Vol. 57, 2016, pp. 345–420.
AI4000 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. Spong, Hutchinson, and Vidyasagar. Robot Modeling and Control. Second Edition, Wiley, 2020.
  2. S. K. Saha. Introduction to Robotics. Second Edition, McGraw Hill Education, 2017.
AI4013 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.
  1. Barocas, Solon, Moritz Hardt, and Aravind Narayanan. Fairness and Machine Learning: Limitations and Opportunities. Online version available.
  2. O'Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books, First Edition, 2016.
  3. Kearns, Michael, and Aaron Roth. The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Oxford University Press, First Edition, 2019.
  4. Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press, First Edition, 2018.
  5. Eubanks, Virginia. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press, First Edition, 2018.
  6. Broussard, Meredith. Artificial Unintelligence: How Computers Misunderstand the World. MIT Press, First Edition, 2018.
AI5000 Machine Learning M.Tech (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. Pattern Recognition and Machine Learning. Springer, 2006.
  2. Shalev-Shwartz, Shai, and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. 2014.
  3. Murphy, K. P. Probabilistic Machine Learning: An Introduction. 2022.
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. 1. Sheldon Ross, Introduction to Probability Models, Academic Press, 2019. 2. Athanasios Papoulis and S. Unnikrishna Pillai, Probability, Random Variables, and Stochastic Processes, Tata McGraw-Hill, 2002. 3. Dimitri P. Bertsekas and John N. Tsitsiklis, Introduction to Probability, Athena Scientific, 2007.
AI5100 Deep Learning M.Tech (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., and H. Bishop. Deep Learning: Foundations and Concepts. Springer, 2023.
  2. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
AI5110 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.
  1. Boyd, Stephen, and Lieven Vandenberghe. Introduction to Applied Linear Algebra. Cambridge University Press, 2018.
  2. Horn, Roger A., and Charles R. Johnson. Matrix Analysis. Cambridge University Press, 2012.
  3. Axler, Sheldon. Linear Algebra Done Right. Springer, 1997.
  4. Strang, Gilbert. Linear Algebra and Learning from Data. Wellesley-Cambridge Press, 2019.
AI5120 Topics in Optimization M.Tech, PhD, and third or final-year B.Tech. 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. Boyd, Stephen, and Lieven Vandenberghe. Convex Optimization. Cambridge University Press.
  2. Bertsekas, Dimitri P. Convex Optimization Theory. Athena Scientific.
Course Details
Course Code Course Name Target Audience Course Contents Suggested References
AI3403 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. Weiss, Gerhard. Multi-Agent Systems - A Modern Approach to Distributed Artificial Intelligence. MIT Press, 1999.
  2. Bullo, Francesco. Lectures on Networked Systems. Edition 1.7, April 2024.
    ISBN: 978-1-986425-64-3.
  3. Lindemann, Lars, and Dimos V. Dimarogonas. Formal Methods for Multi-Agent Feedback Control Systems. MIT Press, 2025.
    ISBN: 9780262382793.
  4. Ditmarsch, Hans van, Wiebe van der Hoek, and Barteld Kooi. Dynamic Epistemic Logic. Springer Publications, 2007.
  5. https://stanford.edu/~boyd/admm.html
AI4010 Online Learning B.Tech IV, M.Tech, 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 technique for expert advice.

Online Learning: Online classification, regret minimization, prediction with expert advice, minimax regret, discounted regret, follow the best expert strategy, tight regret bounds for specific class of loss functions, randomized prediction, calibration, prediction with limited feedback.

Bandit learning: stochastic and adversarial multi-armed bandit settings, EXP3/EXP4 algorithms and their variants, UCB1 and its variants, Thompson Sampling and contextual bandits.

Applications: online spam filtering, clinical trials, game playing, sequential investment/portfolio selection, recommendation systems, universal lossless data compression.
  1. Cesa-Bianchi, Nicolo, and Gabor Lugosi. Prediction, Learning and Games. Cambridge University Press, 2006.
  2. Lattimore, T., and C. Szepesvári. Bandit Algorithms. Cambridge University Press, 2020.
  3. Hazan, Elad. Introduction to Online Convex Optimization. MIT Press, 2022.
AI4803 AI in Finance B.Tech IV, M.Tech, 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.
  1. Prado, Marcos Lopez de. Advances in Financial Machine Learning. Wiley, 1st Edition, 2018.
  2. Tsang, Edward. AI for Finance (AI for Everything). CRC Press, 1st Edition, 2023.
  3. Aldridge, Irene. High Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. John Wiley & Sons, 2013.
    ISBN: 9781118343500.
  4. Dacorogna, et al. An Introduction to High-Frequency Finance. Academic Press, 2001.
    ISBN: 0122796713.
  5. Harris, Larry. Trading & Exchanges: Market Microstructure for Practitioners. Oxford University Press, 2002.
    ISBN: 9780198090540.
AI5040 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.
  1. Narahari, Y. Game Theory and Mechanism Design. 1st Edition, 2016.
  2. Maschler, Michael, Eilon Solan, and Shmuel Zamir. Game Theory. 2nd Edition, 2013.
  3. Nisan, Noam, Tim Roughgarden, Éva Tardos, and Vijay V. Vazirani. Algorithmic Game Theory. Cambridge University Press, 2007.
AI5050 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.
  1. Molnar, Christopher. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 1st Edition, 2023.
  2. Kamath, Uday, and John Liu. Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning. Springer, 1st Edition, 2021.
AI5073 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.
  1. Gerstner, Wulfram, Werner M. Kistler, Richard Naud, and Liam Paninski. Neuronal Dynamics. 1st Edition, 2014.
  2. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
  3. Sterling, Peter, and Simon Laughlin. Principles of Neural Design. MIT Press, 2015.
  4. Breedlove, S. Marc, and Neil V. Watson. Behavioral Neuroscience. 8th Edition, 2017.
  5. Bermudez, José Luis. Cognitive Science. 2nd Edition, 2017.
AI5080 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.
  1. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
  2. Prince, Simon J. D. Understanding Deep Learning. MIT Press, 2023.
  3. Vaswani, Ashish, et al. Attention Is All You Need. NeurIPS, 2017.
  4. Radford, Alec, et al. Improving Language Understanding by Generative Pre-Training. 2018.
  5. Radford, Alec, et al. Language Models are Unsupervised Multitask Learners. 2019.
  6. Ho, Jonathan, et al. Denoising Diffusion Probabilistic Models. NeurIPS, 2020.
  7. Other relevant papers.
AI5090 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.
  1. Gallager, R. G. Stochastic Processes: Theory for Applications. Cambridge University Press, 2013.
  2. Kumar, Anurag. Discrete Event Stochastic Processes. Lecture Notes, Indian Institute of Science (IISc).
  3. Grimmett, Geoffrey, and David Stirzaker. Probability and Random Processes. Oxford University Press, 2020.
  4. Tan, Vincent Y. F. Stochastic Process Lectures.
  5. Moulos, V. Concentration and Sequential Decision Making in Markovian Environments. University of California, Berkeley, 2020.
  6. Küchler, Uwe, and Michael Sørensen. On Exponential Families of Markov Processes. 1998.
AI5133 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.
  1. Fraden, Jacob. Handbook of Modern Sensors: Physics, Designs, and Applications. Springer, 4th Edition, 2010.
  2. Meijer, Gerard, Michiel Pertijs, and Kofi Makinwa. Smart Sensor Systems: Emerging Technologies and Applications. IEEE Press, 2014.
  3. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
AI5153 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.
  1. Corke, Peter. Robotics, Vision and Control. 2nd Edition, 2017.
  2. Siegwart, Roland, Illah R. Nourbakhsh, and Davide Scaramuzza. Introduction to Autonomous Mobile Robots. MIT Press, 2nd Edition, 2011.
  3. Lecture/tutorial slides, handouts, and notebooks.
AI5163 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.
  1. Brooks. Cybersecurity Essentials. Wiley, 2018.
  2. Mitnick, Kevin D., and William L. Simon. The Art of Deception. Wiley, 2011.
  3. Roberts, Scott J., and Rebekah Brown. Intelligence-Driven Incident Response. O'Reilly Media, 2017.
  4. Tsukerman, Clarence Chio. Machine Learning for Cybersecurity Cookbook. Packt Publishing, 2019.
  5. Gil, Clarence, and Joseph Liska. Security with AI and Machine Learning. O'Reilly Media, 2019.
  6. Wu, Ge, and Li. AI and Machine Learning for Network and Security Management. Wiley-IEEE Press, 2022.
  7. Kaur, et al. Artificial Intelligence for Cybersecurity. Information Fusion, 2023.
  8. Li, et al. Differential Privacy. Springer, 2016.
  9. Hu, et al. LLM-TIKG. 2023.
AI5170 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.
  1. Kallianpur, Gopinath. Stochastic Filtering Theory. Springer New York, 1st Edition, 1980.
  2. Kuo, Hui-Hsiung. Introduction to Stochastic Integration. Springer New York, 1st Edition, 2005.
  3. Øksendal, Bernt. Stochastic Differential Equations. Springer Berlin Heidelberg, 6th Edition, 2003.
AI7010 An Overview of Reinforcement Learning PhD 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.
  1. Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second Edition, MIT Press, 2018.
  2. Szepesvári, Csaba. Algorithms for Reinforcement Learning. Morgan & Claypool, 2010.
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