Course Outline: Intelligent Agents and their Environments - The concept of a Rational Agent, Specifying the Task environment (PEAS description), Different characteristics of environments (Fully vs Partially observable, Static vs Dynamic, Episodic vs Sequential etc.) and Different types of agents (Reflex, Goal-based, Utility-based etc.),Search - Formulating a search problem , Uninformed Search strategies: BFS, DFS, DLS, ID-DFS, their working principles, complexities, relative advantages and disadvantages, Informed (heuristic) Search strategies: Greedy Best-first search, A* search: Working principle, Characteristics of heuristics (admissibility and consistency), Proof of A*’s optimality, Local search: Hill Climbing, Searching with non-deterministic actions: AND-OR search trees and Searching with partial observability: Belief state-space search, Adversarial Search - Formulation of a Game tree, The minimax algorithm, Alpha-Beta pruning: Its rationale, working principle and Additional techniques such as Move ordering and Search cut-off, Probabilistic Reasoning - Bayes’ rule and its uses, Bayesian Network: Building a Bayes-net and making inference from it, Markov Chains and Hidden Markov Models: Transition and Sensor models, Building and HMM, applications of HMM, Inference in temporal models: Filtering, Prediction, Most Likely explanations (Viterbi algorithm) etc. and Particle Filters: basic working principle, Making Decisions - Decision theory and Utility theory: Lottery, Utility functions, Maximum Expected Utility principle, Constraints of Utility (Orderability, Transitivity etc) and Markov Decision Processes: Policies, Rewards, Optimal policies and the Utility of States, Value Iteration, Supervised Learning - Basic concepts of classification and supervised learning: Training set, Test set, Overfitting, Underfitting etc., Decision trees: Basic understanding, Learning a Decision tree through entropy calculation, Nearest Neighbor classifier: Basic working principle, Relative advantages and disadvantages, Naive Bayes classifier: Basic working principle, Calculating classification procedures, Relative advantages and disadvantages, Artificial Neural Network: Basic working principle, Basic structure and calculation of a perceptron, Basics of backpropagation algorithm and Support Vector Machines: Basic working principle, Unsupervised Learning (Clustering) - Basic concepts and applications of Clustering, Different types of Clustering: Partitional vs. Hierarchical, Exclusive vs Overlapping vs Fuzzy, Complete vs Partial, K-means Clustering: Basic working principle, characteristics, advantages, disadvantages, Agglomerative Hierarchical Clustering: Basic concepts, Representations (Dendrograms and Nested cluster diagrams), Different techniques to define cluster proximity: Single link, Complete link, Group average, Centroid method, their relative advantages and disadvantages and DBSCAN: Basic principle and applications, Classification of points (Core, Border and Noise), Reinforcement Learning - Understanding basics of Reinforcement Learning: MDPs, Policies, Rewards, Utilities etc., Passive and Active Reinforcement Learning, Exploration and Exploitation, Adaptive Dynamic Programming, Temporal Difference Learning and Q-Learning.