CIS 830: Advanced Topics in Artificial Intelligence

Spring 2000

Hours: 3 hours (extended course project option for 4 credit hours: 3 of CIS 830, 1 of CIS 798)

Prerequisite: First course in artificial intelligence (CIS 730 or equivalent coursework in theory and design of intelligent systems) or instructor permission

Textbook: Artificial Intelligence: A Modern Approach, S. J. Russell and P. Norvig. Prentice-Hall, 1995. ISBN: 013108052

Venue: Monday, Wednesday, Friday 10:30-11:20 Room 236 Nichols Hall

Instructor: William H. Hsu, Department of Computing and Information Sciences

Office: 213 Nichols Hall URL: http://www.cis.ksu.edu/~bhsu E-mail: bhsu@cis.ksu.edu

Office phone: (785) 532-6350 Home phone: (785) 539-7180

Office hours: before class (10-10:30am); 2-2:30pm, Wednesday; 1:30-2:30pm, Friday; by appointment

Class web page: http://ringil.cis.ksu.edu/Courses/Spring-2000/CIS830/

Course Description

This course provides intermediate background in intelligent systems for graduate and advanced undergraduate students. Five topics will be discussed: analytical and multistrategy learning (hybrid analytical, knowledge-driven, and inductive learning), pattern recognition using artificial neural networks, reasoning under uncertainty for decision support, data mining (DM) and knowledge discovery in databases (KDD), and genetic algorithms and evolutionary systems for KDD. Applications to practical design and development of intelligent systems will be emphasized, leading to class projects on current research topics.

Course Requirements

Homework: 4 programming and written assignments (30%)

Papers: 15 (out of 20) written (1-page) reviews of research papers (15%); class presentations (15%)

Examinations: 1 take-home midterm (15%)

Computer language(s): C/C++, Java, or student choice (upon instructor approval)

Project: term programming project for all students (25%); additional term paper or project extension (4 credit hour) option for graduate students and advanced undergraduates

Selected reading (on reserve in K-State CIS Library):

· Neural Networks for Pattern Recognition, C. M. Bishop. Oxford University Press, 1995. ISBN: 0198538499

· Genetic Algorithms in Search, Optimization, and Machine Learning, D. E. Goldberg. Addison-Wesley, 1989. ISBN: 0201157675

Additional bibliography (excerpted in course notes and handouts):

· Machine Learning, T. M. Mitchell. McGraw-Hill, 1997. ISBN: 0070428077

· Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, eds. MIT Press, 1996. ISBN: 0262560976

· Readings in Machine Learning, J. W. Shavlik and T. G. Dietterich, eds. Morgan-Kaufmann, 1990. ISBN: 1558601430

· Readings in Computer Inference and Knowledge Acquisition, B. G. Buchanan and D. C. Wilkins, eds. Morgan Kaufmann, 1993. ISBN: 1558601635

· Probabilistic Reasoning in Intelligent Systems, J. Pearl. Morgan-Kaufmann, 1988. ISBN: 0934613737

· Genetic Programming, J. Koza. MIT Press, 1992. ISBN: 0262111705

· Pattern Classification and Scene Analysis, R. O. Duda and P. E. Hart. John Wiley and Sons, 1973. ISBN: 0471223611

Syllabus

Lecture	Date	Topic	(Primary) Source
0	January 14	Administrivia; overview of topics	RN Chapters 1, 14, 18
1	January 19	Analytical learning: overview	RN 21
2	January 21	Analytical learning: presentation	Thrun/Mitchell
3	January 24	Analytical learning: discussion	Thrun/Mitchell; RN 21.2
4	January 26	Analytical learning: presentation	Brodie/Dejong; TMM 11
5	January 28	Analytical learning: discussion	Brodie/Dejong
6	January 31	Analytical learning: presentation	Chown/TGD; TMM 12
7	February 2	Analytical learning: discussion	Chown/Dietterich
8	February 4	Analytical learning: presentation	Towell et al (TSN)
9	February 7	Analytical learning: conclusions	TSN; RN 21; TMM 12
10	February 9	Artificial neural networks: overview	RN 19; TMM 4
11	February 11	ANNs: presentation	Maclin/Shavlik
12	February 14	ANNs: discussion	Maclin/Shavlik
13	February 16	ANNs: presentation	Sutton; RN 16
14	February 18	ANNs: discussion	Sutton
15	February 21	ANNs: presentation	Hinton et al
16	February 23	ANNs: discussion	Hinton et al
17	February 25	ANNs: presentation	Jordan/Jacobs
18	February 28	ANNs: conclusions	Jordan/Jacobs; RN 16
19	March 1	Uncertainty: overview	RN 15; TMM 6
20	March 3	Uncertainty: presentation	Cheeseman
21	March 6	Uncertainty: discussion; midterm review	Cheeseman
22	March 8	Uncertainty: presentation; midterm	Friedman et al
23	March 10	Uncertainty: discussion	Friedman et al
24	March 13	Uncertainty: presentation	Horvitz et al; RN 17
25	March 15	Uncertainty: discussion; midterm due	Horvitz et al
26	March 17	Uncertainty: presentation	Darwiche/Pearl
27	March 27	Uncertainty: conclusions, DM: overview	Darwiche/Pearl
28	March 29	DM/KDD/decision support: presentation	Fayyad/Haussler/Stolorz
29	March 31	DM/KDD/decision support: discussion	Fayyad/Haussler/Stolorz
30	April 3	DM/KDD/decision support: presentation	John/Kohavi/Pfleger
31	April 5	DM/KDD/decision support: discussion	John/Kohavi/Pfleger
32	April 7	DM/KDD/decision support: presentation	Koller/Sahami
33	April 10	DM/KDD/decision support: discussion	Koller/Sahami
34	April 12	DM/KDD/decision support: presentation	Craven et al
35	April 14	DM/KDD/decision support: conclusions	Craven et al
36	April 17	Genetic algorithms: overview	TMM 9
37	April 19	GAs: presentation	Wilson
38	April 21	GAs: discussion	Wilson
39	April 24	GAs: presentation	Aler/Borrajo/Isasi; Koza
40	April 26	GAs: discussion	Aler/Borrajo/Isasi
41	April 28	GAs: presentation	Raymer et al
42	May 1	GAs: discussion	Raymer et al
43	May 3	GAs: presentation	Horn et al
44	May 5	Conclusions; all projects due	Horn et al; TMM 9
45	May 8	NO FINAL EXAM	RN Sections V-VII

RN: Artificial Intelligence: A Modern Approach, S. J. Russell and P. Norvig

TMM: Machine Learning, T. M. Mitchell

Lightly-shaded entries denote the due date of a paper review.

Heavily-shaded entries denote the due date of a paper review and a written or programming assignment.