Course Description
An introduction to the basic principles, techniques, and applications of Artificial Intelligence. Coverage includes perception and learning, searching and logical inference and knowledge base. Methods used in this course will have wide applications in different artificial intelligent systems such as expert system, robotics, computer vision, and natural language processing. Students will have practical experience in designing and implementing components of an intelligent system.
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 |
