1. Introduction to the Course

  1. Introduction
    • Students investigate non-deterministic computer algorithms that are used in wide application areas but cannot be written in pseudo programming languages.
    • Non-deterministic algorithms have been known as topics of machine learning or artificial intelligence.
    • The topics covered in this course include mainly classical artificial intelligence techniques and soft computing techniques.
    • Classical artificial intelligence techniques include knowledge representation, heuristic algorithms, rule based systems, and probabilistic reasoning.
    • Soft computing techniques include fuzzy systems, neural networks, and genetic algorithms.
    • Some machine learning algorithms for clustering and classification are also included.
  2. Learning objectives
    • Understand the major areas and challenges of AI.
    • Identify problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem.
    • Formalize a given problem in the language/framework of different AI methods.
    • Implement basic AI algorithms.
    • Apply basic AI knowledge and algorithms to solve problems.
    • Design simple software to experiment with various AI concepts and analyse results.
  3. Expectations
    • Self-directed learning
    • Self-motivated learning
  4. Course outline
  5. Evaluation
    • Assignments: 20%
    • Project: 20%
    • Two midterm exams: 40% (20% each)
    • Final exam: 20%
  6. Instructor
    • Dr. Mahnhoon Lee
    • HL 424
    • (250) 377-6022
    • mlee@tru.ca
    • Timetable
  7. How to study well - Is motivation good enough?
    • There are many general ideas. Can you suggest good ideas?
    • Some of them are more important. What are they?
    • There are two types of habits, winning habits and loosing habits.
    • Winning habits are ...
    • Loosing habits are ...