Computer Sciences 540:
Introduction to Artificial Intelligence
(Summer 2003)

Overview | Syllabus | Homeworks | Projects | Exams | Links

What's New

Alan Turing

Course Overview

Class Personnel

Meeting Schedule

1:10 - 2:25 pm, MTWRF, 265 Materials Sciences Building (map)

Course Description

The purpose of this course is to provide an introduction to Artificial Intelligence (AI). A breadth of topics will be covered, including problem solving through search, game playing, logic, planning, machine learning and natural language processing, plus some philosophical and ethical issues. Please see the syllabus for a more detailed breakdown of topics. (Prerequisite: CS 367).


All homeworks and exams must be done individually (however, projects will be done in groups). Cheating and plagiarism will be dealt with in accordance with university procedures. For more specific information about each course requirement, see the subsections below.

Required Textbook

AIMA Artificial Intelligence: A Modern Approach (2nd Edition)
S. Russell & P. Norvig. Prentice Hall, 2002.

This will be our primary and only essential textbook. All reading assignments will either come from this book or from supplementary readings which I will hand out in class. Recommended texts will be on reserve at Wendt Library.

Recommended Texts

HTSI:MH How to Solve It: Modern Heuristics, Z. Michalewicz & D. Fogel. Springer Verlag, 1999.
Excellent extended coverage of problem solving by heuristic search and optimization, as well as two chapters on genetic algorithms (as opposed to three pages in R&N).
ML Machine Learning, T. Mitchell. McGraw-Hill, 1997.
Currently one of the best textbooks for a more comprehensive look at machine learning, which we will cover in the second part of the course. (Also the CS 760 text.)
GEB Godel, Escher, Bach: An Eternal Golden Braid, D. Hofstadter. Basic Books, 1979.
More of a philosphical defense of AI in terms of Kurt Godel's famous incompleteness theorem. Addresses music, art, and the human mind... not light reading, but clever and funny.


Syllabus, Readings, and Lecture Slides

Lecture slides are available here in Adobe PDF format. They are based on my own research, the course texts, and the lecture notes of Chuck Dyer, Mark Rich, and Jim Skrentny.

Homework Assignments

General Homework Information


Class Projects

Presentation Schedule

Monday, 8/4

  1. "Spam Filtering - History and Technique" -- LeMahieu, Parrish
  2. "Survey on applying Artificial Intelligence in Music" -- Hui, Hartono, Wenas
  3. "Spam Detection Methods Using Naive Bayes Filtering" -- Langton, Berns, Fan
  4. "Neural Networks: Tiling and Optimal Damage" -- Foudray, Chua

Tuesday, 8/5

  1. "Survey of Computer Facial Recognition" -- Duffy, Gao, Goentoro
  2. "Machine Learning with Neural Networks and Support Vector Machines" -- Khuu, Lee, Tsai
  3. "Ethics and Artificial Intelligence" -- Saunders, Eastman, King
  4. "Methods of Object Recognition in Computer Vision" -- Gabriel

Wednesday, 8/6

  1. "Genetic Algorithms Applied to the Traveling Salesperson Problem" -- Olsen, Lawinger
  2. "Introduction to the Applications of Support Vector Machines" -- Budig-O'Brien, Howe, Persky
  3. "South Park Character Generator" -- Sijipati, Karnowski
  4. "The Role of Artificial Intelligence in Instruction Scheduling" -- Scherpelz


About the Project

Due date: Friday, August 1 (no late days are allowed)

The class project is an opportunity for you to delve deeper into a particular area of AI that interests you, or to explore aspects that couldn't quite be covered in this excellerated summer course (e.g. bioinformatics, computer vision, speech recognition, robotics, etc.)

Projects should be done in groups of 2 or 3. You may choose to do a strict research paper (i.e. no programming) on a topic, which should be at least 6 pages long. You may also try implementing an algorithm from literature or of your own design, and turn in your code plus a 2-3 page write-up of your goals, methods, experiments and the results. All groups will give a 15 minute presentation in class during the last week. The project grade will depend about half on the presentation, and half on the paper/report.



Exam Schedule

Old Exams

These are exams given by various instructors from the recent past, some provided with solutions. Not all of the material is the same, but they may still help you prepare for my exams. Most are in Adobe PDF format, but a few are in MS-Word.

Some Related Links

Java Programming Help

AI @ UW-Madison

Other Places

© 2003 Burr Settles, UW-Madison.