Code	Course Name	Elective For	Syllabus
DA672	Data-driven System Theory	B.Tech (3rd & 4th year)/M.Tech/Ph.D	Review of dynamical system properties along with differential equations, bifurcation and chaotic systems; Non-linear time series analysis; Review of machine learning methods; Data-driven modeling and dynamical systems (dynamic mode decomposition, Koopman operators, ODE, PDE); Construction of dynamical equation from time series data; State space reconstruction by machine learning methods, examples from model reduction (ODE, PDE), chaotic time series analysis.
DA547	Introduction to Mathematical Biology	B.Tech (3rd & 4th year)/M.Sc/M.Tech/Ph.D	Review of linear systems of ODEs; Phase space analysis; Stability analysis of linear and non-linear system; Concepts of bifurcation; Dynamical models in ecology: population dynamics, single species, interacting species; Dynamical models for diseases: Infectious disease, SIR epidemics, SIS endemics; Dynamical models in cell biology: Gene regulation, cellular differentiation; Spatially structured models, tumor modeling, models for drug delivery; Stochastic processes in biology: Markov chains, birth and death processes, branching processes, Gillespie algorithm.
DA641	Non-linear Regression	B.Tech (4th year)/M.Tech/Ph.D	Moving beyond linearity: Polynomial regression, step functions, basis functions, regression splines, smoothing splines, local regression, generalized additive models; A general non-linear model, non-linear least squares, local and global minimum, contour plots, linear approximation, asymptotic theory of nonlinear least squares; Numerical methods, starting values, non-iterative algorithms: Grid search, random search; Iterative algorithms: Gauss-Newton, Newton-Raphson, Genetic Algorithm, Simulated Annealing; Generalized least squares estimators, maximum likelihood estimators, robust estimation: Least absolute deviation estimation, M-estimation; Bayesian estimation, examples of non-linear regression models; Maximal margin classifier, support vector classifiers, support vector machines; Deep Learning: Single layer neural networks, multilayer neural networks, convolutional neural networks, document classification, recurrent neural network, fitting a neural network.
DA621	Deep Learning for Computer Vision	B.Tech (4th year)/M.Tech/Ph.D	Deep learning review: perceptron, Multi-Layer Perceptron (MLP), backpropagation, PyTorch for deep learning; Convolutional Neural Networks (CNNs): introduction, evolution of architectures, AlexNet, VGG, Inception, ResNet; Understanding CNNs: visualizing filters, gradient with respect to input, style transfer; CNNs for vision: Recognition, detection, segmentation, optical flow, depth estimation; Recurrent Neural Networks (RNNs): review, CNN+RNN models for video understanding; Attention Models: vision and language, image captioning and Visual Question Answering (VQA), Transformer networks; Deep Generative models: review of generative models, Generative Adversarial Network (GAN), Variational Autoencoder (VAE), PixelRNN, image2image transformation applications, inpainting, super-resolution; Recent trends: zero-shot, few-shot, self-supervised learning, meta-learning in vision.
DA622	Robustness and Interpretability in Machine Learning	B.Tech (4th year)/M.Tech/Ph.D	Review of Machine Learning and Deep learning. Robustness: Introduction to adversarial examples, different types of adversarial attacks, FGSM, CW, PGD, universal; Empirical defenses; Different hypotheses surrounding the adversarial examples; Backdoor attacks, data poisoning attacks; Recent advances and competitions/challenges. Interpretability: Understanding and evaluating interpretability; Accuracy-interpretability tradeoff in machine learning; Different types of interpretability approaches, rulebased, prototype-based, feature importance-based, post-hoc explanations, human-in-the loop based; Advanced topics – relation to debugging and fairness in machine learning.
DA771	Machine Learning in Quantum Physics	Ph.D	Fundamentals: Introduction to Material Modeling, Kernel Methods for Quantum Chemistry, and Introduction to Neural Networks; Incorporating Prior Knowledge: Invariances, Symmetries, Conservation Laws: Building Nonparametric n-Body Force Fields Using Gaussian Process Regression, Machine-Learning of Atomic-Scale Properties Based on Physical Principles, Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches, Quantum Machine Learning with Response Operators in Chemical Compound Space, Physical Extrapolation of Quantum Observables; Deep Learning of Atomistic Representations: Message Passing Neural Networks, Learning Representations of Molecules and Materials with Atomistic Neural Networks; Atomistic Simulations: High-Dimensional Neural Network Potentials for Atomistic Simulations, Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights, Active Learning and Uncertainty Estimation, Machine Learning for Molecular Dynamics on Long Timescales; Discovery and Design: Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design, Bayesian Optimization in Materials Science.
DA642	Time-series Analysis	B.Tech (4th year)/M.Tech/Ph.D	Time series and their characteristics; Linear time series analysis and its applications: MA, AR, ARMA, ARIMA ; Spectral analysis: population spectrum, sample periodogram, use of spectral analysis; Estimation and order detection in time series; Conditional heterosdastic models: ARCH, GARCH; Nonlinear models and their applications: TAR, STAR; High frequency data analysis and market microstructure: Models for price change and durations; Multivariate time series and their applications: VAR, cointegrated VAR models; State space models and Kalman filter.
DA671	Introduction to Reinforcement Learning	B.Tech/M.Tech/Ph.D	Introduction to Reinforcement Learning (RL); Markov Chains: Discrete-time Markov chains, Stationary Distribution, Continuous-time Markov Chains. Markov Decision Process (MDP): Terminologies & Fundamentals; Finite Horizon MDP: Dynamic Programming (DP), Bellman Equation; Infinite Horizon Discounted Cost Problems: DP Equation, Fixed Point & Contraction Mapping, Value Iteration (VI), Policy Iteration (PI), Linear Programming formulation; RL Techniques: Multi-armed Bandit; Exploration & Exploitation, Optimism in the Face of Uncertainty, Upper Confidence Bound, Thompson Sampling; Monte-Carlo Methods: First-visit and Every-visit, Monte-Carlo Control, Off-policy Monte Carlo Control; Temporal Difference Learning: TD Prediction, Optimality of TD(0), SARSA, Q-learning, n-step TD Prediction, TD (lambda) algorithm, Linear Function Approximation, Linear TD (lambda); Policy Search Methods: Policy Gradient (PG) Theorem, REINFORCE, Variance Reduction in PG Methods, Actor-critic Methods, Batch RL.
DA674	Advanced Topics in Reinforcement Learning	B.Tech/M.Tech/Ph.D	Review of Markov Chain; Discounted Cost Markov Decision Process (MDP): Finite Horizon MDP, Infinite Horizon MDP; Stochastic Shortest Path Problems; Infinite Horizon Average Cost Problems: Value Iteration Algorithm (VIA), Relative VIA, Policy Iteration Algorithm, Continuous-time MDPs, Uniformization; Semi-MDP; Partially Observable MDP (POMDP). Reinforcement Learning (RL): Q-learning, Post-Decision State Learning, RL for semi-MDP and POMDP; Deep RL: DQN, A2C, A3C, Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO); Distributed RL, Game Theoretical RL; Multi-armed Bandit: KL-UCB, Rested & Restless Bandit, Adversarial Bandit, Contextual Bandit; Real-world use cases.
DA526	Image Processing with Machine Learning	M.Tech/Ph.D	Fundamentals of machine learning: dataset generation, augmentation, standardization, train/validation/test set preparation, cross-validation, model training and evaluation; Supervised and unsupervised learning, regression and classification, artificial neural networks, deep architectures. Image processing with Machine Learning: Introduction to image processing; Machine learning workflow for image processing; Introduction to software tools for image processing and machine learning; Elements of visual perception, imaging geometry; Image acquisition: depth of field, auto exposure, high dynamic range imaging; Image processing in spatial and frequency domains, super-resolution; Image restoration: deblurring, dehazing, inpainting; Image segmentation: semantic segmentation; Color image processing, pseudo coloring; Image representation and image descriptors; Image recognition: localization and classification; Machine learning in video processing.
DA546	Introduction to Statistical Learning	M.Tech/Ph.D	Correlation: the scatter diagram, the correlation coefficient, potential problems with correlation coefficient: outliers, non-linear association, association and causation. Simple and multiple linear regression: least squares fitting, computational issues in fitting the model, hypothesis testing on the coefficients, interval estimation, prediction of new observations, assessing the accuracy of the model, detection of outliers, multicollinearity, variable selection, predictions. Classification problems: an overview, logistic regression, Bayes classifier, linear discriminant analysis, quadratic discriminant analysis, naive Bayes, k-nearest neighbour, support vector machines. Re-sampling methods for model assessment: testing and training data, cross validation, leave-one-out-cross-validation, k-fold cross validation, bias-variance trade off; bootstrapping, jackknife. Tree-based methods for regression and classification: an overview, basics of decision trees, regression trees, classification trees.
DA673	Neural Data Analysis	B.Tech/M.Tech/Ph.D	Peripheral sensory processing, hearing and vision, sensory transduction, spiking neurons, cortical organization; Leaky integrate and fire (LIF) neuron, McCulloch-Pitts model, perceptron, artificial neural networks; Model types; Neural code, information theory, efficient coding hypotheses; Neuroimaging via EEG, physiological basis of EEG, EEG measurement; Preprocessing EEG, signal and noise, spectral analysis, artifacts identification, bad channel identification, application of ICA, Kalman filtering; Analyzing EEG signals, event-related potentials (ERPs), filtering, Hilbert transform, topographic maps, time-frequency analysis; Classification, single-trial classification, common spatial patterns (CSP), stimulus reconstruction approaches; Other neuroimaging modalities, reaction times, pupillometry, MEG, fMRI.
DA623	Computing with Signals	B.Tech/M.Tech/Ph.D	Introduction to signals, sensory signals, atmospheric signals, chemical signals; Sensory processing; Signal representations, continuous and discrete-time, basis functions, Fourier series, Taylor series, linear algebra fundamentals, LTI system, convolution, sampling, DFT, DCT, time-frequency analysis; Dimentionality reduction, PCA, dictionary learning, compressive sensing; Spectral estimation; Filtering and filtering artifacts, Kalman filtering; Modelling, model fitting, bias-variance trade-off, regularization, cross-validation; Generative models, Gaussians, GMM, HMM, maximum likelihood, Expectation-maximization, Bayesian estimation; Classifiers, neural networks; Applications in audio, image, EEG, weather monitoring.
DA675	Fuzzy Systems and Applications	B.Tech/M.Tech/Ph.D	Introduction to fuzzy logic and systems, brief history, uncertainty, imprecision, and vagueness, classical sets, fuzzy sets, classical logic vs. fuzzy logic, membership functions, properties, operations, defuzzification, extension principle in fuzzy logic. Classical vs. fuzzy relations, representation, types, composition of fuzzy relations, properties, fuzzy relations in decision-making. Basic principles of inference in fuzzy logic, fuzzy if-then rules, canonical form of fuzzy rules, approximate reasoning with fuzzy logic, fuzzy inference engines, Mamdani fuzzy model, Takagi-Sugeno-Kang fuzzy model, Tsukamoto fuzzy model, generalized fuzzy model, defuzzification techniques, uncertainty handling in fuzzy inference, adaptive neuro-fuzzy inference system. Fuzzy system components, rule base construction, design options for fuzzy systems, fuzzy system optimization and tuning, adaptive fuzzy systems, real-time applications in control systems, computer vision, decision support.
DA626	Recmmendation System Design	B.Tech/M.Tech/Ph.D	History of Recommendation System, Matrix Factorization, Collaborative Filtering, Context-Based Filtering, Hybrids Methods, Nearest Neighbors, Graphical Neural Network, Evaluation methods of recommendation system (several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity). Linguistic and statistical techniques for text mining and content analysis, Semantic Web and ontologies, Semantic Recommendation systems. Ethical aspects of Recommendation systems.
DA651	Artificial Intelligence for Next-Generation Wireless Systems	M.Tech/Ph.D	Next-generation wireless communication basics: Physical layer functionalities, Medium access control and network layer functionalities; AI basics: Supervised learning, Unsupervised learning, Reinforcement learning, Deep neural networks; AI-based wireless communication techniques: Spectrum Access and Sharing, Radio Resource Allocation, Energy efficient communications, Adaptive Modulation and coding, Caching, Cross-layer optimization; AI use cases in Next-generation wireless communication: Multiple Input Multiple Output (MIMO), Mobile Edge Computing (MEC), Internet of Things (IoT), Heterogeneous Networks
DA625	Special Topics in Natural Language Processing	B.Tech/M.Tech/Ph.D	Attention and Transformers, Meta-Learning, Zero/Few Shots, Lifelong learning, Bayesian meta-learning, contrastive learning, Knowledge Distillation, Transfer learning, Different Paradigms of LLMs, Reasoning in LLMs, Training Objectives, Chatbots, Diffusion Models, Recent Publications in ACL, EMNLP, NAACL, ICLR, NeurIPS, ICML, etc.
DA624	NLP with Large Language Models	B.Tech/M.Tech/Ph.D	History of evolving NLP problems, natural language processing: Words, Syntactic Parsing, Dependency Parsing; n-gram Language Model; natural language understanding language generation, Neural models basics: Recurrent Neural Network, Attention and Transformer; Neural Language Models, Different formulations: Encoder only, Encoder-decoder, Decoder only; training and fine-tuning Large Language Models: BERT, RoBERTa; ChatGPT; LLama; Prompt Engineering; Case studies and future challenge
DA527	Neuromorphic Artificial Intelligence	B.Tech/M.Tech/Ph.D	Biophysical foundation: Neuron Models, Synapse Models, Neural Coding; Learning algorithms: Sparse coding, Representation learning, Attractors, and Vector Symbolic Architectures, spike timing dependent plasticity; Neuromorphic deep learning: Surrogate gradient, sigma-delta networks, hardware in the loop, and binary neural networks, deep neural network to a spiking neural network conversion, neural architecture search, learning to learn; TinyML: algorithmic and software optimizations for inference, on-device learning, emerging hardware, privacy, and security; Frameworks for deployment: Intel Lava, Neuromorphic Intermediate Representation framework; Applications: wake words recognition, keyword spotting, radar-based gesture recognition.