Course Description

Course Information


Topics Descriptions # of lectures
Introduction Intelligent agents: a discussion on what Artificial Intelligence is about and different types of AI agents 2
Search Optimization on a Discrete state-space – Uninformed and informed search methods – BFS, DFS, IDS, A*, and IDA* search methods 3
Constraint Satisfaction Search Constraint Satisfaction Problems (CSP), Arc consistency algorithm 3
Local search Hill Climbing, Simulated Annealing, Genetic algorithms, Swarm intelligence – Particle Swarms, Ant Colony Optimization 3
Logical Reasoning Propositional logic, Reasoning – Forward and Backward Chaining, *First order Logic and Reasoning 2
Optimization Review of Linear Algebra and Calculus for Multivalued Functions. Optimization of multivariable functions, Directional Derivatives, Gradient, Hessian, Gradient-based Optimization, Numerical Differentiation 3
Machine Learning I Supervised learning, Regression, Classification methods – formulation of Linear Regression, Logistic regression, Linear classifiers 3
Machine Learning II Neural Networks, Backpropagation, Regression and Multiclass Classification, Training of Neural Networks 3