Fall 2000 276A Pattern Analysis and Machine Intelligence Boelter Hall 9436 TH 2-3:50

Instructor: Allen Klinger, Professor, Computer Science <klinger@cs.ucla.edu> 310 825-7695 direct; Office: 3531-H Boelter Hall

Secretary: Cynthia Bautista; <cynthia@cs.ucla.edu> 310 825-2660; Office: 3731-G Boelter Hall

Textbooks

Bishop, C., Neural Networks For Pattern Recognition, Oxford Univ. Press, 1995; also see Nilsson below.

Duda, R. and Hart, P., Pattern Classification and Scene Analysis, Wiley, 1974; see below.

Breiman, L., Classification and Regression Trees, Wadsworth, 1984; also see Devvijver or Fukunaga below.

Coward, L. Andrew, Pattern Thinking, Praeger, 1990; also see Adams below.

Devlin, Keith, Mathematics: The Science of Patterns, W. H. Freeman and Co., 1994; also see Polya below.

Description

Pattern Analysis and Machine Intelligence (PAMI) involves three main areas: Statistical Pattern Recognition, Learning Processes, and Structural Methods. This course introduces them and several application-oriented subtopics: both theoretic, Feature Extraction and Selection, Classification Rules, and practical, Vision, Image Processing, Understanding, Speech/Character Recognition, Motion, etc. The course involves text and research readings. Mathematics presented in the course suffices. Previous experience there is not required though a prior probability course would be helpful.

Requirements

Midterm examination, class presentations based on recent PAMI research (twenty-thirty minute talks), and term-project: written report, project-review talk. Term-project written report due last class meeting.

Organization

Course lectures, supplemental readings, and student talks are fundamental "Textbooks" act as a resource not the final word. Topics focus on fundamental problems. Specifics on pattern recognition (PR) and statistical learning from notes, Duda/Hart, and Nilsson books. Related mathematical issues presented in class. More than one class presentation required of all students. Interaction and discussion encouraged.

Course grades are based on student presentations, in-class comments, examinations, and written submissions. The major written submission is the term-project report. They are due at the end of the tenth week, December 3 and accepted only until December 5. The best policy is to confer with the instructor in office hours about the report content, papers read, etc., at least twice in the ten week term.

The general course outline on the next page is a structured framework for the course. Individual needs, class interests, and the many fields that involve patterns will govern the actual nature of the course. The course is to introduce students to a challenging research realm.

The last item listed at each week are reading assignments in Bishop or Duda/Hart (italic font), given in chapter (Roman numeral): page (Arabic numerals) format. Items in Bishop or Duda-Hart suitable for student presentation in class have pages underlined. Other items suitable for presentations may be taken from books (see below), IEEE Transactions (e.g., Pattern Analysis and Machine Intelligence - see below); and other sources. A term paper based on student selected items, including research papers, is a course requirement. At least one paper read/presented to the class must be from a refereed journal.

Week Dates/Class Meetings Topic Assignment

1-3 9/28-10/17 Statistical Pattern Recognition Basics

1 9/28, 10/3 Overview, Decisions I: 1-26

2 10/5, 10 Gaussian, Discriminant Models II: 33-42; III: 77-89

3 10/12, 17 Risk and Loss Models, Bayes I: 26-28; II: 42-46

4-6 10/19-11/5 Statistical Pattern Recognition Advanced Topics

4 10/20, 22 Nonparametric Decisions II: 47-73

5 10/27, 29 Perceptrons III: 89-105

6 11/3, 5 Midterm Fisher Discriminant III: 105-112, V: 138-172

7-9 11/10, 26 Structural Methods Conclude, Report

7 11/10, 12 Multi-Layer and Clustering IV: 116-133, VI: 211-225

8 11/17, 19 Higher-Order, Back-Prop IV: 133-148

9 11/24, 26 Representation Notes, IX: 327-378

10 12/1, 3 Feature Selection VIII: 304-14, Report 12/3

11 12/9 Scheduled Final Tuesday 3:00 - 6:00 PM.

Books, Handbooks, Paper Reprint Volumes, and Monographs

Nilsson, N., The Mathematical Foundations of Learning Machines, with introduction by T. Sejnowski and H. White, San Mateo CA: Morgan Kaufman Publishers, 1990. Previously published as Learning Machines, 1965.

Sebestyen, G., Decision-Making Processes in Pattern Recognition, NY: Macmillan, 1962.

Fukunaga, K., Introduction to Statistical Pattern Recognition, NY: Academic Press, 1972.

Zurada, J., Introduction to Artificial Neural Systems, NY: West Publishing Company, 1992.

Devijver, P. and Kittler, J., Pattern Recognition; a Statistical Approach, Englewood Cliffs, NJ: Prentice-Hall, 1982.

Grenander, U., Lectures in Pattern Theory, Vol. 1, 2, and 3, NY: Springer-Verlag, 1976.

Fu, K-S., Syntactic Methods in Pattern Recognition, NY: Academic Press, 1974.

Ahuja, N. and Schachter, B., Pattern Models, NY: Wiley, 1983.

Agrawala, A. (ed.), Machine Recognition of Patterns, NY: IEEE Press, 1977.

Young, T. and Fu, K-S. (eds.), Handbook of Pattern Recognition and Image Processing, NY: Academic Press, 1986.

Duda, Richard O., Hart, Peter E., Stork, David G., Pattern Classification NY: John Wiley & Sons; ISBN: 0471056693, $100.00, 2nd edition, Nov 2000, to appear.

Polya, George, Mathematics and Plausible Reasoning (Induction and Analogy in Mathematics, Vol. I, Patterns of Plausible Inference, Vol. II), Princeton, New Jersey: Princeton University Press, 1954.

Adams, James, Conceptual Blockbusting, NY, W.W. Norton & Co., 1979.

Key Journals and Papers

IEEE Transactions on Pattern Analysis and Machine Intelligence

Pattern Recognition

Minsky, M. "Steps Toward Artificial Intelligence", Proc. IRE, 8-30, Jan. 1961, also in Feigenbaum, E. and Feldman, J. (eds.), Computers and Thought, NY: McGraw-Hill, 1963.

IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 19, 1997, TK 7882 P3 I11

Aug 19/8 ...Fast Pattern Matching... Uenohara, Kanade

...Bayean Belief Networks... van Engelen

July 19/7 ...Object Representation Moghaddam, Pentland

June 19/6 ...Rejection Rule Ha

...Pattern Classifiers Hsu/Way

May 19/5 ...Decision Trees Esposito, Malerba, Semeraro

Apr 19/4 ...Fingerprint Verification Jain, Hong, Bolle

Mar 19/3 ...Nearest Neighbor Classifier Ojouadi, Bouktache

Feb 19/2 ...User Sketches Del Bimbo, Pak

...Cluster-Based Templates Hochberg, Kelly, Thomas, Kerns

...Linear Discriminant ... Aladjem

Jan 19/1 ...Algebraic Invariants Mickel, Nandhakenmar, Velter

....Nearest Neighbor... Hamamoto, Uchimura, Tomita

We use the term probability to mean several things, but the simplest notion is simply the ratio number of favorable cases to total number. I.e., if a six-sided cube is tossed the probability of a "1" face is 1/6.

(The following background material presents intuitively-understandable probability examples. One way to view this is as leading to numeric pattern recognition cases - see Devlin.)

Easily-understood probability examples come from games like dice and poker. For example,

... approximate odds against getting each hand. Odds are based on a five card hand as dealt to one player....Odds against a rare "royal flush" - ace, king, queen, jack, ten, all of one suit - are 650,000 to one.

One pair 1 1/3 to 1 Flush 500 to 1

Two pairs 20 to 1 Full house 690 to1

Three of a kind 45 to 1 Four of a kind 4,000 to 1

Straight 250 to 1 Straight flush 72,000 to 1

[from Smithsonian, Aug 97, p.60.]

Likewise rolling dice, solid symmetrical objects, enable equally probable outcomes via cubes, tetrahedrons, dodecahedrons, etc.