Artificial-Intelligence-and-Expert-System

All the materials(notes, slide, handouts, books, code), you will find in the course home page.

Course Home

- Mid Term
+ Lecture 1: Chapter 1 -- Introduction
+ Lecture 2: Chapter 2 -- Intelligent Agents
+ Lecture 3: Chapter 3 -- Solving Problems by Searching: 3.1 Problem-Solving Agents, 3.2 Example Problems
+ Lecture 4: Chapter 3 -- Solving Problems by Searching: 3.3 Searching for Solutions, 3.4 Uninformed Search Strategies
+ Lecture 5: Chapter 3 -- Solving Problems by Searching: 3.5 Informed (Heuristic) Search Strategies, 3.6 Heuristic Functions
+ Lecture 6: Chapter 5 -- Adversarial Searchc, Minimax Algorithm, Alpha-beta prunning

- Final Term
+ Lecture 7: Chapter 4 -- Beyond Classical Search: 4.1 Local Search Algorithms and Optimization Problems(Hill-climbing Search, Simulated Annealing, Local Beam Search.)
+ Lecture 8: Chapter 4 -- Beyond Classical Search: 4.1.4 Genetic algorithms
+ Lecture 9: Chapter 6 -- Constraint Satisfaction Problems
+ Lecture 10: Chapter 13 14 -- Quantifying Uncertainty, Probabilistic Reasoning
+ Lecture 11: Chapter 15 -- Probabilistic Reasoning over Time: 15.1 Time and Uncertainty, 15.2 Inference in Temporal Models, 15.3 Hidden Markov Models
+ Lecture 12: Chapter 18 -- Learning from Examples: 18.1 Forms of Learning, 18.2 Supervised Learning, 18.6 Regression and Classification with Linear Models, 18.7 Artificial Neural Networks