15-381 Artificial Intelligence: Representation, Problem Solving and Learning

Syllabus for 15-381

Time Tuesdays and Thursdays 1:30 to 2:50 pm
Place Doherty Hall 2315
Instructors Jaime Carbonell (jgc@cs.cmu.edu)
David Cohn (cohn@justresearch.com)
TAs
(scroll down to see office hours)
Greg Aist (aist@cs.cmu.edu)
Brian Bailey (babailey@andrew.cmu.edu)
Ulas Bardak (ubardak@andrew.cmu.edu)
Chuck Rosenberg (chuck@cs.cmu.edu)
Bryan Singer (bsinger@cs.cmu.edu)
Weng-Keen Wong (wkw@cs.cmu.edu)
Administrator Jennifer McGuiggan (jenm@cs.cmu.edu)
NSH 4517
Textbook Artificial Intelligence: A modern Approach
by Russell and Norvig, Prentice Hall, 1995
(Note: Lecture notes are very important too)
Newsgroup cmu.cs.class.cs381


Updates and Announcements

Watch this space for important updates and announcements!

May 15, 2000
The Final Exam Answer Key is available
May 10, 2000
A practice exam is available
. Note that this final covers material that we did not cover in class.
May 1, 2000
Lecture notes on speech recognition are online
April 27, 2000
Solutions for Assignment 3 are available
April 26, 2000
The handin directory for the miniproject is in:
/afs/andrew/scs/cs/15-381/handin/miniproject/user_id
If there is no disk space available, you will have to make your project available on afs or over the web. Make sure you set the permissions and inform the TA in charge of your project.
April 24, 2000
Notes on Reinforcement Learning are now available online
April 18, 2000
Notes on Unsupervised Learning are now available online


Resources

Office Hours

  Monday Tuesday Wednesday Thursday Friday
10:00          
10:30     Bryan Singer
Wean Hall
8203
  Chuck Rosenberg
Wean Hall
7130
11:00      
11:30      
12:00 David Cohn
Newell-Simon Hall
4632
       
12:30        
1:00   Weng-Keen Wong
Wean Hall
4112
   
1:30   15381 Class
Doherty Hall
2315
15381 Class
Doherty Hall
2315
 
2:00    
2:30      
3:00 Brian Bailey
Wean Hall
3130
       
3:30        
4:00   Jaime Carbonell
Newell-Simon Hall
4519
   
4:30   Greg Aist
Newell-Simon Hall
4215
Ulas Bardak
Newell-Simon Hall
4632
 
5:00    
5:30      
6:00          

Material Covered

The core content of Artificial Intelligence is covered, including: problem-solving and search, logic and knowledge representation, probabilistic reasoning methods, machine learning methods. Major application areas are explored briefly (natural language, information retrieval, machine vision, industrial optimization, etc.), as outlined in the class schedule.

Prerequisites

15-212 or equivalent computer-science proficiency is required. Some knowledge of algorithms (e.g. indexing, "big-O") and some mathematics (logic, probability, or linear algebra) will help. Ability to program well on your own is important.

Homeworks and grading

We expect to have 4 homeworks, 3 of which combine programming and problem-sets, and the last will be a team-based mini-project. We will have a midterm and a final examination. Grading will be as follows:

Homework50% (includes mini-project)
Midterm20%
Final30%

Anyone with an A- or better average, including the mini-project homework (assuming it is turned in early enough for grading before the final), will be exempted from taking the final and will receive an A for the course. All others will take the final exam. So doing homework well, early and often is the best antidote to the otherwise inevitable end-of-semester crunch.


Last Update: May 10, 2000 12:38
by Weng-Keen Wong