Course Description
Introduction to principles and programming techniques of artificial intelligence, strategies for searching, representation of knowledge and automatic deduction, learning, adaptive systems, and applications of artificial intelligence.
Pre-requisites
305214: Fundamental of Data Structures and Algorithms
Course Outline
This course covers the following topics (tentative):
- Search
- Graph Search
- Constraint Satisfaction
- Games
- Machine Learning
- Nearest Neighbors
- Decision Trees
- Neural Networks
- SVM
- Knowledge Representation and Inference
- Propositional and First Order Logic
- Rule-based Systems
- Natural Language
Assessment
| Assessment Item | Worth |
|---|---|
| Assignments | 40% |
| Midterm Exam | 20% |
| Final Exam | 40% |
Resources
Required readings come directly from the course lecture notes, and the recommended textbook for this course is:
Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. 2nd edition. Upper Saddle River, NJ: Prentice Hall, 2003. ISBN: 0137903952.
Classes Timetable
| Class | Time | Location |
|---|---|---|
| AI (13-1) | Mon 13.00-14.50 | EE 602 |
| AI (13-2) | Wed 08.00-09.50 | EE 113 |
| AI (14-1) | Mon 15.00-16.50 | EE 106 |
| AI (14-2) | Wed 16.00-17.50 | EN 305 |
Staff
Woralak Kongdenfha
Extension 4355