University of Saskatchewan Department of Computer Science

Welcome to the Department of Computer Science

Courses >
Printer

Computer Science 317 Detailed Information

Note that the information presented here does not necessarily reflect the most up to date syllabus or course information. Rather this information is intended to provide a general overview of course content from previous offerings.

Instructor

Gord McCalla (GM), Thorvaldson 281.4, 966-4902, <mccalla@cs.usask.ca>

Office hours: by appointment (to arrange a time send me an e-mail or chat to me after class)

Other Instructors

Jim Greer (JG): will teach a couple of the main modules in the course

several faculty members and grad students will give guest lectures (guess the initials!)

Time & Place

MWF 9:30 –10:20, Thorvaldson 124

Textbook

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, 2010, 1132 pages.

Workload

4 assignments, each worth 15%

Final exam worth 40%

Content

This course is a general introduction to artificial intelligence, oriented around the autonomous agent metaphor. The topics covered in some detail include:

  • foundations and history – what is AI and where did it come from?
  • the agent metaphor – why might somebody think a computational agent can think?
  • problem solving by computer – how can an agent search through a multitude of possibilities to find a solution to a problem?
  • knowledge representation and reasoning – how can an agent know something about its world and reason about that knowledge?
  • uncertain knowledge and reasoning – how can an agent deal with an uncertain world?
  • learning – how can an agent learn more about its world?
  • natural language understanding – how can an agent communicate with us in a natural language like English?
In addition there will be many special lectures on various AI topics, provided by a variety of Department faculty and graduate students. Occasional tutorials may be provided, as well. A projected course outline follows, although this is possibly subject to some change.
Date Topic Lecturer Assignments
Sept. 8 Intelligent Systems GM  
Sept. 10 Intelligent Systems GM  
Sept. 13 Problem Solving by Searching JG  
Sept. 15 Problem Solving by Searching JG  
Sept. 17 Problem Solving by Searching JG  
Sept. 20 Problem Solving by Searching JG  
Sept. 22 Problem Solving by Searching JG  
Sept. 24 Special: Deep Blue and Game Playing GM  
Sept. 27 Knowledge and Reasoning GM  
Sept. 29 Knowledge and Reasoning GM  
Oct. 1 Knowledge and Reasoning GM  
Oct. 4 Knowledge and Reasoning GM A1 due
Oct. 6 Knowledge and Reasoning GM  
Oct. 8 Knowledge and Reasoning GM  
Oct. 13 Knowledge and Reasoning GM  
Oct. 15 Special: Constraint Satisfaction MH  
Oct. 18 Uncertain Knowledge and Reasoning JG  
Oct. 20 Uncertain Knowledge and Reasoning JG  
Oct. 22 Special: Causality EN  
Oct. 25 Uncertain Knowledge and Reasoning JG A2 due
Oct. 27 Uncertain Knowledge and Reasoning JG  
Oct. 29 Uncertain Knowledge and Reasoning JG  
Nov. 1 Learning TP  
Nov. 3 Learning TP  
Nov. 5 Special: Data Mining TP  
Nov. 8 Natural Language Understanding GM  
Nov. 10 Natural Language Understanding GM  
Nov. 12 Natural Language Understanding GM  
Nov. 15 Natural Language Understanding GM A3 due
Nov. 17 Natural Language Understanding GM  
Nov. 19 Special: CALL PW  
Nov. 22 Applications: AIED GM  
Nov. 24 Applications: User Modelling GM  
Nov. 26 Special: Affect and Motivation JV  
Nov. 29 Special: Vision and Image Processing ME  
Dec. 1 Special: Game AI KS  
Dec. 3 Philosophy: Strong vs. Weak AI, The Singularity, Wrap Up GM A4 due