CIS 830: Advanced Topics in Artificial Intelligence

Spring 2003

 

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) and basic database systems background, or instructor permission

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

Venue: 4:30PM - 5:20PM Monday/Friday, 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                              ICQ: 28651394

Office hours:

In N127: 12:00 – 12:45 Monday

At office: 15:00 - 15:45 Monday, 09:00 - 10:30 Wednesday; by appointment

Class web page: http://www.kddresearch.org/Courses/Spring-2003/CIS830/

 

Course Description

 

            This course provides intermediate background in intelligent systems for graduate and advanced undergraduate students.  Four topics will be discussed: fundamentals of data mining (DM) and knowledge discovery in databases (KDD), artificial neural networks for regression and clustering in KDD, reasoning under uncertainty for decision support, 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: 3 of 4 programming and written assignments (15%)

Papers: 8 (out of 12) written (1-page) reviews of research papers (8%); project presentations (10%)

Class participation: in-class discussion, quiz (2%)

Examinations: 1 in-class midterm (25%), no final exam

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

Project: term programming project for all students (40%); 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):

 

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

·          Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. I. H. Witten and E. Frank.  Morgan Kaufmann.  ISBN: 1558605525

 

Additional bibliography (excerpted in course notes and handouts):

 

·          Pattern Classification, 2nd Edition, R. O. Duda, P. E. Hart, and D. G. Stork. John Wiley and Sons, 2000. ISBN: 0471056693

·          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

·          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

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

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


Syllabus

 

Lecture

Date

Topic

(Primary) Source

0

18 Jan 2002

Administrivia; overview of topics

RN Chapters 1-9, 14, 18

1

23 Jan 2002

AI topics review

RN Chapters 1-9, 14, 18

2

28 Jan 2002

Introduction to machine learning

TMM 2-5, 7; RN 18

3

30 Jan 2002

Data mining basics

WF 1-2

4

04 Feb 2002

G: Intro to Genetic Algorithms

TMM 9, Goldberg

5

06 Feb 2002

B: Bayesian Networks

TMM 6, Goldberg

6

11 Feb 2002

N: Artificial Neural Networks (ANNs)

TMM 6, Goldberg

7

13 Feb 2002

K: DM/KDD/decision support overview

WF 3-4

8

18 Feb 2002

Discussion K1: 1 of 12

WHH

9

20 Feb 2002

Presentation K1: 1 of 12

Roby Joehanes

10

25 Feb 2002

Discussion A1: 2 of 12

WHH

11

27 Feb 2002

Presentation A1: 2 of 12

Siddharth Chandak

12

04 Mar 2002

Discussion B1: 3 of 12

WHH

13

06 Mar 2002

Presentation B1: 3 of 12

WHH

14

11 Mar 2002

Discussion G1: 4 of 12

WHH

15

13 Mar 2002

Presentation G1: 4 of 12

WHH

16

25 Mar 2002

Midterm review

RN Chapters 1-9, 14, 18-21

17

27 Mar 2002

Midterm exam

RN Chapters 1-9, 14, 18-21

18

01 Apr 2002

Discussion/presentation K2: 5 of 12

WHH

19

03 Apr 2002

Discussion/presentation A2: 6 of 12

WHH

20

08 Apr 2002

Discussion/presentation B2: 7 of 12

Yousheng Chang

21

10 Apr 2002

Discussion/presentation G2: 8 of 12

Indira Mohanty

22

15 Apr 2002

Discussion/presentation K3: 9 of 12

Vinod Chandana

23

17 Apr 2002

Discussion/presentation A3: 10 of 12

WHH

24

22 Apr 2002

Discussion/presentation B3: 11 of 12

WHH

25

24 Apr 2002

Discussion/presentation G3: 12 of 12

Sreenivas Babu

26

29 Apr 2002

Conclusion

TBD

27

01 May 2002

Project presentations I; projects due

N/A

28

06 May 2002

Project presentations II

N/A

29

08 May 2002

Project presentations III;

N/A

30

10 May 2002

NO CLASS; REVIEWS DUE

N/A

 

WF: Data Mining, I. H. Witten and E. Frank

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 written or programming assignment (always on a Friday).  Assignments are assessed a late penalty beginning the day of the next class (a Monday).  Note: to be permitted to turn in an assignment past the Friday due date, a student must notify the instructional staff (cis830ta@www.kddresearch.org) in writing between the shaded date and the due date