CIS 830: Advanced Topics in Artificial Intelligence
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/
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.
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
·
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
·
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
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