CMPT 399 Outline 2014/15 Winter

COURSE NUMBER:

CMPT 399

COURSE TITLE:

Special Topics in Computing Science - 2014/15 Winter - Introduction to Artificial Intelligence

NAME OF INSTRUCTOR:

Michael Janzen

CREDIT WEIGHT AND WEEKLY TIME DISTRIBUTION:

credits 3(hrs lect 3 - hrs sem 0 - hrs lab 3)

COURSE DESCRIPTION:

A course on a topic of figure of special interest to a member of the computing science faculty and offered on a non-recurring basis.

Prerequisites: CMPT 250; some topics may have more specific prerequisites

2014/15 Winter

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.

REQUIRED TEXTS:

Russell, Stuart and Peter Norvig (2010), Artificial Intelligence: A Modern Approach 3 rd Ed. Prentice Hall.

MARK DISTRIBUTION IN PERCENT:

Laboratory Assignments	30%
Project	15%
Paper/Group Discussion	10%
Midterm Examination	15%
Final Examination	30%

	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 techniques and their limitations
Implement several artificial intelligence approaches
Discuss implications of artificial intelligence in society

COURSE 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

Bayesian Networks

probabilities, conditional probabilities

graph triangulation

causality

Fuzzy Logic

set membership

Neural Networks

feed forward networks

back propagation

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.