Module-1: Artificial Intelligence

Approaches to AI: Turing Test and Rational Agent Approaches; State Space Representation of Problems, Heuristic Search Techniques, Uninformed search (BFS, DFS, Dijkstra), Informed search (A* search, heuristic functions, hill-climbing), Adversarial search (Game Playing : Minimax algorithm, Alpha-beta pruning) , Local Search, Cutoff Procedures, Constraint Satisfaction Problems: Factor Graphs, Backtracking Search, Dynamic Ordering, Arc consistency.

Knowledge Representation: Logic (Propositional and Predicate Calculus), Semantic Networks, Frames, Rules, Scripts, Conceptual Dependency and Ontologies; Expert Systems, Handling Uncertainty in Knowledge (Quantifying uncertainty, Probabilistic Reasoning, Probabilistic Reasoning over time, Multi‐agent decision making), Probabilistic Reasoning over time: Markov Decision Processes: Policy evaluation, Policy improvement, Policy iteration, Value iteration, Uncertain Reasoning: Probabilistic reasoning, Bayesian Networks (Hidden Markov Models, Kalman Filters, Dynamic Bayesian Networks), Dempster-Shafer theory, Fuzzy logic.

Planning: Components of a Planning System, Linear and Non Linear Planning; Goal Stack Planning, Hierarchical Planning, STRIPS, State space search, Planning Graphs , Partial Order Planning, Automated Planning

Reinforcement Learning: Passive RL, Active RL, Generalization in RL, Policy Search, Deep Reinforcement Learning, Monte Carlo, SARSA, Q‐learning, Exploration/Exploitation, Function approximation, Deep re‐inforcement learning

Natural Language Processing: Rule-based systems, Grammar and Language; Parsing Techniques, Semantic Analysis and Pragmatics.

Multi Agent Systems: Agents and Objects; Agents and Expert Systems; Generic Structure of Multiagent System, Semantic

Web, Agent Communication, Knowledge Sharing using Ontologies, Agent Development Tools.

Fuzzy Sets: Notion of Fuzziness, Membership Functions, Fuzzification and Defuzzification; Operations on Fuzzy Sets, Fuzzy Functions and Linguistic Variables; Fuzzy Relations, Fuzzy Rules and Fuzzy Inference; Fuzzy Control System and Fuzzy Rule Based Systems.

Genetic Algorithms (GA): Encoding Strategies, Genetic Operators, Fitness Functions and GA Cycle; Problem Solving using GA, Evolutionary Computation: Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Differential Evolution.

Conversational AI, Explainable AI, Understanding AI Ethics and Safety

Reference Books

• S. Russel and P. Norvig. Artificial Intelligence: A Modern Approach (Third Edition),Prentice Hall, 2009
• E. Rich and K. Knight, Artificial Intelligence, Addison Wesley, 1990
• Yang, Q. (1997), Intelligent Planning: A decomposition and abstraction based approach, Springer Verlag, Berlin Heidelberg
• Sutton and Barto. Reinforcement Learning: An Introduction.

Module-2: Machine Learning

Supervised Learning: 

    Introduction: Motivation, Different types of learning, Linear regression, Simple Linear regression,  Multiple  Linear regression, Ridge regression, Logistic regression 

   Classifiers: Decision trees, nearest neighbor classifiers, generative classifiers like naive Bayes, linear discriminate analysis, Support vector Machines, Linear regression, Logistic regression, bias-variance trade-off

   Gradient Descent: Introduction, Stochastic Gradient Descent, Subgradients, Stochastic Gradient Descent for risk minimization

   Support Vector Machines: Hard SVM, Soft SVM, Optimality conditions, Duality, Kernel trick,  Implementing Soft SVM with Kernels

   Feature selection techniques: wrapper and filter approaches, back‐ward selection algorithms, forward  selection algorithms, PCA, LDA

   Decision Trees: Decision Tree algorithms, Random forests

   Cross-Validation Methods: Leave-One-Out (LOO) Cross-Validation, k-folds Cross Validation,

   Neural Networks: Feedforward neural networks, Expressive power of neural networks, Multi-layer  perceptron, SGDand Backpropagation

   Model selection and validation: Validation for model selection, k-fold cross-validation, Training-Validation-Testing split, Regularized loss minimization

    Unsupervised Learning and Generative Models:

   Nearest Neighbour: k-nearest neighbour, Curse of dimensionality

   Clustering: Linkage-based clustering algorithms, k-means algorithm, Spectral clustering ,hierarchical, EM, K‐medoid, DB‐Scan, cluster validity indices, similarity measures

   Graphical models: HMM, CRF, MEMM

   Dimensionality reduction: Principal Component Analysis, Random projections, Compressed sensing Generative Models: Maximum likelihood estimator, Naive Bayes, Linear Discriminant Analysis, Latent variables and Expectation-maximization algorithm, Bayesian learning

    Feature Selection and Generation: Feature selection, Feature transformations, Feature learning

    Computational Learning Theory and Deep Neural Networks: 

    Statistical Learning Framework: PAC learning, Agnostic PAC learning, Bias-complexity tradeoff, No free lunch theorem, VC dimension, Structural risk minimization, Adaboost

    Foundations of Deep Learning: DNN, CNN, RNN, Autoencoders

Reference Books:

•  Shalev-Shwartz,S., Ben-David,S., (2014), Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press
• Mitchell Tom (1997). Machine Learning, Tata McGraw-Hill
• Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006
• Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. MIT Press 2012

Module-3: Deep Learning

     Introduction to Deep Learning

     Prerequisite: Linear Algebra, Probability, Numerical Computation, Machine Learning Basics

     Deep Networks: Attention layers, CNN, Gated CNNs, Graph Neural Networks, RNN

     Model Search: Optimization, Regularization, AutoML

     Applications: Practical Methodology and  Neural language models

     Representation Learning & Structured Models

     Linear Factor Models: Autoencoders

     Representation Learning: Unsupervised pre-training, transfer learning and domain adaptation,    distributed representation, discovering underlying causes

     Structured Models: Learning about dependencies, Inference and approximate inference, sampling    and Monte Carlo Methods, Importance Sampling, Gibbs Sampling, Partition Function, MAP inference  and Sparse Coding, Variational Inference

     Deep Generative Models

     Deep Generative Models: Deep Belief Networks, Variational Autoencoder, Generative Adversarial  Network (GAN), Deep Convolutional GAN, Autoencoder GANs, iGAN, pix2pix, CycleGAN,  Conditional GANs, StackGAN

Reference Books:

• Ian Goodfellow, YoshuaBengio and Aaron Courville. Deep Learning. MIT Press 2016
• Yoav Goldberg. 2016. A primer on neural network models for natural language processing. J. Artif. Int. Res. 57, 1 (September 2016), 345‐420.
• R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. J. Spiegelhalter. "Probabilistic Networks and Expert Systems". Springer‐Verlag. 1999.

Module-4: Machine Learning Mathematics

•  Overview of supervised and unsupervised learning 
• Undirected graphical models: overview, representation of probability distribution and conditional independence statement, Factorization, CRFs, Applications to NLP, Markov networks.
• Directed graphical models: Bayesian networks.
• Deep Networks for Sequence Prediction: Encoder‐decoder models (case study translation), Attention models, LSTM, Memory Networks
• Deep Network for Generation: Sequence to Sequence Models – VariationalAutoencoders – Generative Adversarial Networks (GANs) – Pointer Generator Networks – Transformer Networks Time series forecasting: models and case‐studies 
• Modern clustering techniques: Multi‐objective optimization for clustering, Deep learning for clustering Online Learning, Mistake Bounds, Sub‐space clustering Meta‐learning and federated learning concepts: tools and techniques Case‐studies: Recent topics for solving various problems of natural language processing, bioinformatics, information retrieval

Reference Books:

• Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
• Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.
• Data Mining: Concepts and Techniques by Jiawei Han, MichelineKamber, Morgan Kaufmann Publishers.
• A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988.