Intelligent Agents: Agents and Environments, Good Behaviour: The Concept of Rationality, The Nature of Environments, The Structure of Agents.

Problem Solving: Game Paying.

Uncertain knowledge and Reasoning: Quantifying Uncertainty, Acting under Uncertainty , Basic Probability Notation, Inference Using Full Joint Distributions, Independence , Bayes’ Rule and Its Use The Wumpus World Revisited.

Neural Network Representation: Problems – Perceptrons – Multilayer Networks and Back Propagation Algorithms – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning.

Neural networks and genetic algorithms: Brief history and Evolution of Neural network, Biological neuron, Basics of ANN, Activation function, MP model.

Recommender System: Datasets, Association rules, Collaborative filtering, User-based similarity, item-based similarity, using surprise library, Matrix factorization.

Text Analytics: Overview, Sentiment Classification, Naïve Bayes model for sentiment classification, using TF-IDF vectorizer, Challenges of text analytics.

Clustering: Introduction, Types of clustering, Partitioning methods of clustering (k-means, k-medoids), hierarchical methods.

Instance Based Learning: Introduction, k-nearest neighbour learning(review), locally weighted regression, radial basis function, cased-based reasoning.