Course Web Page: www.cs.wpi.edu/~nth/cs534/home.html
Office Hours: Tuesday 3 PM or by appointment.
This is an introductory graduate AI course. During the semester we will cover general knowledge representation techniques and problem solving strategies. Topics will include search, intelligent agents, game playing, rule-based systems, logic programming, frames(or semantic networks), planning, and uncertain reasoning. Prerequisites: A familiarity with data structures and their analysis (Big O) and a recursive high-level language. Knowledge of LISP is an advantage. For the catalog description of this course see the WPI Graduate Catalog.
This class will require about 15 hours of work a week.
Tuesday 6pm – 8:50 pm.
Third Floor of Fuller Labs FL 320
Students are also encouraged to attend the AIRG Seminar Thursdays at 11 am. First one is Sept 9th.
Students are required to attend class. If you can’t make it, please send me email. If you miss more than two classes, you grade will be dropped by a letter grade. If you miss than 4 it will go done two grades. Etc.
Prof. Neil Heffernan
nth@cs.wpi.edu
Office: FL 237
Phone Number: (508) 831-5569
Office Hours: Tuesday 3 PM or by appointment. If you plan to come to office
hours, but not right at 3 PM, send me some email letting me knowing you are
coming and I make sure I am there, otherwise I might think no one is coming to
office hours.
I check email once a day, so don’t expect an instantaneous response a few hours before a program is due.
Other speakers will occasionally be invited to lecture to the class.
There will be a total of 2 exams. Each exam will cover the material presented in class since the beginning of the semester. In particular, the final exam is cumulative. There will be material from the readings that is never discussed in class, so please keep up with the reading. Both will be in-class exams.
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Midtem Exam 1 |
20% |
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Final Exam 2 |
20% |
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Homeworks and Quizzes
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60% |
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Class Participation |
Extra Points |
Homeworks and Projects: The homeworks and projects are designed for your learning. Some homeworks will be just written, while most will have some programming component. Not all assignments will be worth the same amount. I will determine the relative worth of the assignments after assessing the time required to complete them. More detailed descriptions of the assignments and projects will be posted to the course webpage at the appropriate times during the semester.
Un-announced pop quizzes will be given on the reading. Please do the reading.
You can talk about programming assignments together, but not share code. This should be crystal clear. If in doubt, ask me.
Warning: I get personal angry at cheating, and deal with it as strongly as possibly. I spend a good deal of time trying to detect it, and if you cheat in my course, I will work to have to you expelled from the University. It goes without saying that any type of cheating will be penalized with an F grade for the course and will be reported to the WPI Judicial Board in accordance with the Academic Honesty Policy.
Late homework will not be accepted unless you have received prior permission from the instructor. Even then, late homeworks will generally receive a one letter grade reduction.
Students are expected to read the material assigned to each class in advance and to participate in class. Class participation will be taken into account when deciding students' final grades.
You need to check your email at lest once a day, and changed will be posted there. The mailing list for this class is:
If you do not get email from within the first week you are probably not on the list and should send me email.
The web pages for this class are located at http://www.cs.wpi.edu/~nth/cs534/home.html
Announcements will be posted on the web pages and/or the class mailing list, and so you are urged to check your email and the class web pages frequently.
It is not sufficient to just hand in code. You need to show the testing you did of your program, as well as explain how it works. Pointes will be taken off for code that is not commented. You also need to indent your code is some understandable fashion.
The following additional references complement and/or supplement the material contained in the required textbook.
1. Tom M. Mitchell "Machine Learning" McGraw-Hill, 1997.
2. P. Langley "Elements of Machine Learning" Morgan Kauffamann Publishers, Inc. 1996.
· Once you get to know Lisp, the book you will want is Guy Steele’s Common Lisp the Language. You can buy a copy, or use the online version. You can download html pages of the whole text here. Unfortunately, you can’t browse the book using the table of contents online because of an error, so just download the html sources and view them.