Fall 2008
Introduction to Artificial Intelligence
Course Number: CSC384H
Course Web Site: www.cdf.utoronto.edu/~csc384h/fall/
Lectures: Mondays and Wednesdays, 10 a.m. to 11 a.m.
Lecture Location: MP 102
Tutorials: Fridays, 10 a.m. to 11 a.m.
Tutorial Location: MP 102
Instructor: Sonya Allin
Email: s.allin@utoronto.ca
Office Hour: Fridays, 3 p.m. to 5 p.m.
Office Hour Location: Bahen 2200
TAs:
Jasper Snoek, jasper@cs.utoronto.edu
Christian Fritz, fritz@cs.utoronto.edu
Maryam Fazel-zarandi, mfazel@cs.utoronto.edu
TA office hours will be posted to the course website in the first week of class.
There will be no tutorial in the first week of class.
Intro
duc
tion
This course provides an introduction to some major topics in the field of Artificial Intelligence. The first half of the course will cover a variety of “intelligent” search strategies and
ways that such strategies can be applied to two player games. We will also cover “classical”
approaches to logic and planning that make use of binary valued variables. In the second half
of the course we will introduce probabilistic methods to both reason and make plans. More
specifically, we will introduce Bayesian Networks and tools to plan in uncertain conditions.
Pre-requisites
It will be helpful to have had an introductory class in statistics. Something like STA 250 or
STA 255/247/257 would fit the bill. It will also be helpful to have some familiarity with
Prolog, as one or two programming assignments will make use of this. We will, however,
provide some Prolog tutorials. The only required preCourse Work
4 Assignments
1 Midterm
Introduction to Artificial Intelligence - Syllabus Fall 2008 1
Fall 2008
Introduction to Artificial Intelligence
requisite for this course relates to your CGPA. You will be expected to learn necessary
background material that was not covered in your prior courses on your own.
Course Text Book
Artificial Intelligence: A Modern Approach (2003), 2nd Edition, Stuart Russell and
Peter Norvig
The textbook is recommended, but not required. Lecture notes will cover much of the
material that is in the textbook. You can also look at the textbook in the engineering
and computer science library; two copies have been placed on 24 hour reserve.
1 Final
Assignments will be worth a total of 50% of the total grade (12.5% each). Exams (midterm
and final) will be worth 50% of the total grade (15% for the midterm, 35% for the final). The
final will be cumulative with an emphasis on the second half of the course.
Late Policy/Make-Up Exams
The general policy is this: late assignments will be docked 10% for every day they are overdue. After 5 days, a late assignment will not be accepted.
It is advisable to start your assignments early, so that you can get a feel for how much time
they are going to take you to complete. Don’t wait until the last minute to start an assignment, especially one that involves programming. This will only cause you pain, suffering and
sadness.
If you have a legitimate reason that you need to be late on an assignment, contact the course
instructor. Please contact her as early as possible; not at midnight the night before the assignment is due.
If there is a (legitimate and debilitating) medical reason that you cannot attend an exam, let us
know and we will arrange a make up exam date for you.
Bulletin Board
There will be a very sparsely monitored bulletin board located at:
https://csc.cdf.toronto.edu/bb/YaBB.pl?board=CSC384H1F
Plagiarism
Obviously, don’t do it; it is a serious academic offense. You can help one another with assignments or work together, but don’t give away code or answers to questions. If you are
really stuck on a homework question, don’t panic ... just come and talk to the instructor or
one of the TAs.
For details on the meaning of plagiarism and how it is dealt with at this university, see:
http://www.cs.toronto.edu/~fpitt/documents/plagiarism.html
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Fall 2008
Introduction to Artificial Intelligence
The bulletin board will be primarily a tool for students to communicate with one another, not a forum to ask the instructor questions. If you have questions, please email
them directly to the instructor or to one of the TAs. The website will be the primary
tool by which you will find information about changes to the course syllabus, assignment clarifications, etc.
(Tentative) Course Schedule
Section 1: Searching
Week 1
Week 2
Week 3
Week 4
Introduction and beginning of Search lectures
Searching
Game Play
Constraint Satisfaction Problems
Section 2: Knowledge Representation and Planning
Week 5
Logical variables, operators
Week 6
Logical inference, proof
Week 7
Classical planning
Section 3: Reasoning in Uncertain Conditions
Week 8
Probability review / fundamentals
Week 9
Intro to Bayesian Networks
Week 10
Bayesian Network Construction
Section 4: Planning in Uncertain Conditions
Week 11
Expected Utility and Policies
Week 12
Intro to Decision Networks
Week 13
Constructing Decision Networks
The midterm will be in Week 7 of class. You will have roughly 2 weeks to complete each
assignment.
The first assignment will be handed out the second week of classes.
Some Important Dates to Remember
September 21
October 13
October 17
November 3
December 5
December 22
Last day to add courses
Thanksgiving (No class)
Schedule for finals determined
Last day to drop courses
Last day of courses
Winter holidays begin
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