CMP140 Course Description
Winter 1999

This course offers an introduction to artificial intelligence, with emphasis on reasoning under uncertainly and machine learning. Topics include logic, Bayesian inference nets, Bayes decision theory, artificial neural networks, search algorithms and game playing. Examples will be taken from the application of artificial intelligence to the Human Genome Project.

Prerequisites for the course are CMPS101 and any other upper division CS or Math course.

The requirements for the course are: project or paper, homework and exams (50%). The final exam will be 8AM to 11AM Monday March 15. It is OK to work in teams on the project, but each individual's contribution must be documented/credited. The final exam is mandatory. No incompletes will be given.

Policy on late homework: Homework can be turned in late only up to the next class session after it was due (e.g. if it is due Thurs, we will accept it late on the following Tues.). After this time, no further late homework will be accepted. Late homework gets only 50% credit. We will drop one lowest homework score when computung the grade for homeworks.

The text for the class is is "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig, published by Prentice Hall, 1995. Comments, Errata, lecture notes and code used in the text book are available from the author. Additional readings may be put on reserve in the Science Library.

Syllabus

Week Topic Reading in text
1 Introduction Pages 1-52
2 Searching Pages 55-115
2+3 Logic, Knowledge Representation and Reasoning Pages 149-180, 185-216, 265-285
4 Machine Learning Pages 525-560
5 Neural Networks Pages 563-596
6 Uncertainty Pages 415-433
7 Probabilistic Reasoning Systems Pages 436-467
8 Making Decisions Pages 471-521
9 Game Playing Pages 122-145
10 Future of AI


Questions regarding about page content should be directed to cline@cse.ucsc.edu.
Last modified January 21, 1999.

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