CPSC 063 - Fall 2009

Artificial Intelligence

Tuesday/Thursday 7:00pm-8:30pm, Science Center 264

Course Info

News and Updates

Course Description and Syllabus

The syllabus for the course is available here.

Contact Info and Office Hours

Professor: Eric Eaton
Office: I will be borrowing Lisa Meeden's office in the evenings, Science Center 243
Office Hours: Tuesdays and Thursdays 6:00pm-6:50pm, and by appointment.
E-mail: (Please put "CS63" at the start of the subject line)

E-mail is the best way to reach me, and I make a concerted effort to respond to all e-mails within 24 hours (often, much less!). Primarily, I will be on-campus and available in the evenings, although we can make other special arrangements as needed. During the day, I work as a research scientist at a local AI research laboratory. Often, it may not be possible for me to respond to e-mails weekdays between 8am - 5 pm, so most replies will come in the evenings.








1 Tues 9/01 Course overview; What is AI? skim Ch. 1, McCarthy paper, Dartmouth Proposal,
Seibel Ch. 1 (Graham Ch. 1)
HW1 out Slides
Thurs 9/03 Agents/Lisp

Ch. 2, Graham article,
Seibel Ch. 2, skim 3, 4-5 (Graham Ch. 2)

  Intelligent Agents Slides, Lisp slides
Lisp lab
2 Tues 9/08 Problem solving as search Ch. 3.1-3.3,
Seibel Ch. 6-7,10-11 (Graham Ch. 3-4)
Lisp debugging handout
Thurs 9/10 Uninformed search Ch. 3.4-3.7
Seibel Ch. 12-13 (Graham Ch. 5, Appendix A)

Slides (see above)
Lisp Quick Reference

3 Tues 9/15 Informed search Ch. 4.1-4.2
Seibel Ch. 14, "multiple values" section of Ch. 20 (Graham Ch. 7)
HW1 due;
HW2 out
Thurs 9/17 Optimization and local search Ch. 4.3, Genetic Algorithms   Slides
4 Tues 9/22 Game playing Ch. 6.1-6.3   Slides
Thurs 9/24 Game playing II Ch. 6.4-6.7, Schaeffer article   Slides (see above)
Logical Reasoning and Planning
5 Tues 9/29 Knowledge-based agents Ch. 7.1-7.4 HW2 due;
HW3 out; Project description out
Thurs 10/01 Propositional and first-order logic Ch. 7.5, skim 7.7, Ch. 8.1-8.3   Slides
6 Tues 10/06 Logical inference Ch. 9 Project teams formed  Slides
Thurs 10/08 Knowledge representation and State-space planning Skim Ch. 10.1-10.2, skim 10.6, Ch. 11.1-11.2 HW4 out Slides
  Tues 10/13 Fall Break
Thurs 10/15 Fall Break
7 Tues 10/20 Partial-order planning Ch. 11.3 HW 3 due

Slides (see above)

Thurs 10/22 Scheduling and hierarchical planning / review Ch. 12.1-12.2 Project proposal due Slides
8 Tues 10/27 MIDTERM
Reasoning Under Uncertainty and Machine Learning
8 Thurs 10/29 Probabilistic reasoning Ch. 13.1-13.8
9 Tues 11/03 Bayesian networks Ch. 14.1-14.2, 14.4 (section on inference by enumeration only), 20.1-20.2 (through section on Naive Bayes Models) HW4 due;
HW5 out
Thurs 11/05 Decision trees Ch. 18.1-18.2, skim 18.3, Mitchell Ch. 3   Slides
10 Tues 11/10 Decision trees / Reinforcement Learning Ch. 21.1-21.3 Project design due
RL Slides
Thurs 11/12 Reinforcement Learning Ch. 21.5-21.6   Slides (see above)
11 Tues 11/17 k-nearest neighbor, naive Bayes, boosting 18.4, skim 20.4 HW5 due; HW6 out Slides
Thurs 11/19 Support vector machines and kernel methods 20.6-20.7, Bennett article   Slides
12 Tues 11/24 Unsupervised and semisupervised learning 20.3 (excluding the parts on learning Bayes Nets and Hidden Markov Models) Tournament dry run #1 Slides
Thurs 11/26 Thanksgiving Break
Miscellaneous Topics
13 Tues 12/01 Philosophy and history of AI Chronology of AI; Ch. 26, Turing article; Searle article    
Thurs 12/03 AI in Games / Emerging AI techniques TBA HW6 due; Tournament dry run #2 Slides
14 Tues 12/08 Project presentations / Tournament
Dec. 15 @ 7pm FINAL EXAM Project and final report due

Exam equation sheet

Course survey (html, pdf)

* Special topics courses are subject to change.

Useful Resources