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COURSE NUMBER: CMPT 375
COURSE TITLE: Introduction to Artificial Intelligence
NAME OF INSTRUCTOR: Michael Janzen
CREDIT WEIGHT AND WEEKLY TIME DISTRIBUTION: 3 credits (3 hrs lecture - 0 hrs seminar - 3 hrs lab)
COURSE DESCRIPTION: This course introduces students to the field of Artificial Intelligence (AI) focusing on game playing, constraint satisfaction problems, and uncertain reasoning. AI algorithms enable computers to compete with humans in games such as Chess, Checkers, and Go. Constraint satisfaction problems search large solution spaces for answers meeting requirements.Uncertain reasoning enables inferences using incomplete knowledge. Throughout the course students will reflect on the relationship between human intelligence and artificial intelligence.

Prerequisites: CMPT 370
COURSE TEXTBOOK: Russell, Stuart and Peter Norvig (2010), Artificial Intelligence: A Modern Approach 3rd Ed. Prentice Hall.
MARK DISTRIBUTION IN PERCENT:
Laboratory Assignments 30%
Project15%
Paper/Group Discussion10%
Tests 20%
Final Exam 25%

100%
COURSE OBJECTIVES: This course is intended to introduce students to the area of artificial intelligence.
Upon successful completion of this course students should be able to:
  • Explain several artificial intelligence techniques and their limitations
  • Implement several artificial intelligence approaches
  • Discuss implications of artificial intelligence in society
TOPIC OUTLINE:
  • Foundations and History
    • What is artificial intelligence?
    • Hard AI versus soft AI
  • Game Playing
    • Evaluation functions and heuristics
    • Minimax trees, alpha-beta pruning, and scouting
    • Transposition tables
    • Go and Monte Carlo techniques
    • Rule books and endgame databases
  • Constraint Satisfaction Problems
    • Hard constraints
    • Constraint propagation and consistency
    • Partial constraints
    • Semi-ring based constraint satisfaction
  • Uncertain Knowledge and Reasoning
    • Neural Networks
      • feed forward networks
      • back propagation
    • Bayesian Networks
      • probabilities, conditional probabilities
      • graph triangulation
      • causality
    • Fuzzy Logic
      • set membership
  • Additional Topics As Time Allows
    • Genetic Algorithms
      • string representation
      • mutation and crossover
      • competition
    • Logic and Rule Based Systems
      • Solving through searching
    • Classification:
      • Decision Trees
      • Support Vector Machines


Required texts, assignments, and grade distributions may vary from one offering of this course to the next. Please consult the course instructor for up to date details.

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