CS486/686 - Fall 2008  Introduction to Artificial Intelligence

Instructor: Pascal Poupart
Email: ppoupart [at] cs [dot] uwaterloo [dot] ca
Course website: http://www.student.cs.uwaterloo.ca/~cs486
Newsgroup: uw.cs.cs486
Office Hours: Wednesdays 9:30-11:30am (DC2514)
Lectures: Tuesday & Thursday 14:30-15:50 (MC1056)
Teaching Assistants:


The design of automated systems capable of accomplishing complicated tasks is at the heart of computer science.  Abstractly, automated systems can be viewed as taking inputs and producing outputs towards the realization of some objectives.  In practice, the design of systems that produce the best possible outputs can be quite challenging when the choice of outputs is constrained, the consequences of the outputs are uncertain and/or dependent on other systems, the information provided by the inputs is incomplete and/or noisy, there are multiple (possibly competing) objectives to satisfy, the system must adapt to its environment over time, etc.  This course provides an introduction to Artificial Intelligence, covering some of the core topics that underly automated reasoning.  The modeling techniques that will be covered are quite versatile and can be used to tackle a wide range of problems in many fields including natural language processing (e.g., topic modeling, document clustering),  robotics (e.g.,  mobile robot navigation),  automated diagnosis (e.g., medical diagnosis, fault detection), data mining (e.g., fraud detection, information retrieval), operations research (e.g., resource allocation, maintenance scheduling), assistive technologies, etc.


The topics we will cover include:

  1. Introduction to Artificial Intelligence
  2. Search and Problem Solving
    1. Uninformed Search
    2. Informed Search
    3. Constraint Satisfaction Problems
    4. Propositional Logic
    5. First-order Logic
  3. Reasoning Under Uncertainty
    1. Probability Theory
    2. Bayesian Networks
    3. Utility Theory
    4. Decision Networks
    5. Markov Networks
    6. Markov Logic Networks
  4. Machine Learning
    1. Inductive Learning
    2. Decision Trees
    3. Ensemble Learning
    4. Statistical Learning
  5. Other areas of Artificial Intelligence
    1. Natural Language Processing
    2. Assistive Technologies

Course Organization:

The course material will be covered primarily in lectures. You should also read the appropriate chapters of the textbook assigned for each lecture. When lecturing with slides, I will make my lecture slides available online in PDF format. These are not intended to replace your lecture notes, but to supplement them and help you organize them.

There will be four assignments given in the course.  Each assignment will have a theoretical part and a programming part. The approximate out and due dates are:

Assignments will consist of programming and theoretical questions.  You are free to program in the language of your choice, however Matlab is recommended since it provides a convenient high-level programming environment for matrix operations.  Assignments are done individually (i.e., no team). For each assignment, a hard copy must be handed in on the due date either in class or in the assignment drop off box.  No late assignment will be accepted.

There is also a course project that is required for graduate students registered in CS686 and optional for undergraduate students registered in CS486.   The project will consist of investigating a research problem (related to Artificial Intelligence) chosen by the student.

There will be a midterm test on November 4th.  There will also be a final examination to be scheduled by the registrar. 

Grading Scheme


CS686 (graduate students only):


The textbook for CS486/686 is Artificial Intelligence: A Modern Approach (2nd Edition), Prentice Hall, by Russell and Norvig.  This will be the main reference for the course.  A few copies are currently on reserve at the library (call #UWD1615).  Readings in the textbook are assigned for every lecture in the course schedule.