Artificial Intelligence
(CSC 361)
Students should check these pages at least once a week for important information.
Course Description
This course provides a general introduction to AI (Artificial Intelligence): Its techniques and its main sub-fields. It gives an overview of underlying ideas, such as search, knowledge representation, expert systems and learning.
Recommended Books
1. “Artificial Intelligence: A modern approach”, 3/E
Stuart Russell, Peter Norvig, Prentice Hall, 2010
2. "Artificial Intelligence - Structures and Strategies for Complex Problem Solving", George F. Luger, Pearson Internationl Edition, 2009
3. “Artificial Intelligence Illuminated”
Ben Coppin, Jones and Bartlett Publishers, 2004
4. “Artificial Intelligence: A new synthesis”
Nils Nilsson, Morgan Kaufmann, 1998
5. “Introduction to expert systems”
Peter Jackson, Addison Wesley, 1999
6. Lecture slides and your lecture notes!
Grading
MT1 20%
MT2 20%
Final exam 40%
Homework, Quizzes, Project 20%
Detailed Syllabus
Chapter 1 
Introduction
Chapter 2 Problem Solving
2.1 Search
- Problem formulation
- Uninformed Search (BFS, DFS, DLS, IDS, UCS)
- Informed Search (Greedy Search, A*, IDA*)
- Optimization (Hill Climbing,TS, SA, BS, GA, GP)
2.2 Constraint Satisfaction Problems (CSP)
Chapter 3 Knowledge Representation
3.1 knowledge representation and reasoning
3.2 Propositional Logic
3.3 First Order Logic
Chapter 4 Expert Systems & Prolog
4.1 Introduction to Expert System
4.2 prolog
Chapter 5 Learning (Decision Trees, ANN, Reinforcement Learning)
5.1Introduction to Neural Networks
Chapter 6 Game Playing
Assignments
1. Problem Formulation: Assignment, Solution
2. Blind Search : Assignment, Solution
3. Heuristic Search : Assignment, Solution
4. Optimization: Assignment, Solution
5. Constraint Satisfaction Problems: Assignment, Solution
6. Knowledge Representation & Expert System: 
Assignment, Solution
7. Machine Learning: Assignment, Solution
8. Game Playing
Exams - Solutions (Samples)
Selected Topics for Presentation
Additional Information