CSC384H1F: Introduction to Artificial Intelligence
Fall 2009
Course Name: Introduction to Artificial Intelligence
Course Number: CSC384H1F
Course Web Site: www.cdf.utoronto.edu/~csc384h/fall/
Lectures: Mondays and Wednesdays, 1 to 2 pm
Lecture Location: Bahen 1210
Tutorials: Fridays, 1 to 2 pm
Tutorial Location: Bahen 1210
Instructor: Sonya Allin and Toby Hu
Email: csc384instr [at] cs [dot] toronto [dot] edu.
Office Hours: For Sonya: Fridays, 4-5 pm, BA 2200
Office Hour Location: Bahen 2200
TAs:
Maryam Fazel-zarandi ( mfazel [at] cs [dot] toronto [dot] edu
)
Abdel-rahman Mohamed (asamir [at] cs [dot] toronto [dot] edu)
Torsten Hahmann (th-09 [at] cs [dot] toronto [dot] 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.
Introduction
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. 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 fits 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-pre requisite for this course relates to your CGPA. We cannot waive this; you will have to go to the undergraduate office to waive it. You will be expected to learn any 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
CSC384H1F Syllabus, Fall 2009

CSC384H1F: Introduction to Artificial Intelligence
Fall 2009
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.
Course Work
3 Assignments
1 In-Class Midterm
1 Final Project
Assignments will be worth 45% of the total grade (15% each). The final project will be worth 35% of your grade, and the midterm worth 20%.
Late Policy/Make-Up Exams
The general policy is this: late assignments will be docked 20% 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 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, debilitating) reason that you cannot attend an exam, let us know as soon as possible.
Bulletin Board
There will be a sparsely monitored bulletin board located at: https://csc.cdf.toronto.edu/bb/YaBB.pl?board=CSC384H1F
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.
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 problem, don’t panic ... just come and talk to the instructor or one of the TAs.
CSC384H1F Syllabus, Fall 2009

CSC384H1F: Introduction to Artificial Intelligence
Fall 2009
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
(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 3: Reasoning in Uncertain Conditions
Week 5 Probability review / fundamentals
Week 6
Week 7
Intro to Bayesian Networks
Bayesian Network Construction
Section 2: Knowledge Representation and Planning
Week 8 Review of Logics
Week 9
Week 10
Logical Inference and Proof
Classical Planning
Section 4: Planning in Uncertain Conditions
Week 11 Expected Utility and Policies
Week 12
Week 13
Making Decisions with Uncertainty
Conclusion
The Midterm will be on November 16 th .
You will have roughly 2 weeks to complete each assignment. The first assignment will be handed out in the second week of classes.
Details on the project will be handed out in the third week of class. A project proposal will be due in the 6th week of class. Project presentations will take place in Weeks 12 and 13.
Some Important Dates to Remember
September 15 Last day to add courses
October 12
October 23
Thanksgiving (No class)
Schedule for finals determined
November 3
December 4
December 21
Last day to drop courses
Last day of courses
Winter holidays begin
CSC384H1F Syllabus, Fall 2009

CSC384H1F: Introduction to Artificial Intelligence
Fall 2009
CSC384H1F Syllabus, Fall 2009