Artificial Intelligence and Machine Learning (CSC602)
1 unit: · L-T-P 3-1-0.
Detailed syllabus
Unit 1
Introduction to Artificial Intelligence (AI): What is Intelligence, Reasoning and Planning, Learning and Adaptation, and interaction with the real world, A brief history of AI, Application areas of AI, State of the art. (2) Problem solving by search: Problem types, Illustrative search problems; Search Space, Search tree; BFS, DFS, UCS; Greedy search, Local search; Hill climbing; Heuristics; A* search; Admissibility and consistency of heuristics, Game trees; Minimax search; Alpha-beta pruning. (7) Knowledge Representation: Propositional, predicate logic, first order logic, resolution and unification; logic programming. (6) Reasoning under Uncertainty: Conditonal independence representation, exact inference through variable elimination, and approximate inference through sampling. (5) Machine Learning: (i) Supervised Learning: Regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naïve Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross validation, multi-layer perceptron, feed-forward neural network. (12) (ii) Unsupervised Learning: Clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis. (10)
Source: official document