Fall 2002 276A Pattern Analysis and Machine Intelligence

Instructor: Allen Klinger, Professor, Computer Science <klinger@cs.ucla.edu>

Office: 4531-C Boelter Hall (310) 825-7695 direct www.cs.ucla.edu/~klinger

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: theoretic - Feature Extraction and Selection, Classification Rules - and practical - Vision, Image Processing, Understanding, Speech/Character Recognition, Motion, etc.

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.

Course lectures and selected text readings. Student talks - book topics, research papers). Course notes on pattern recognition fundamental problems. Selected items from a wide variety of book sources. Mathematical subjects presented in class: statistical experience not required (knowledge of probability helpful). Student talks based on recent IEEE Trans. PAMI material required during the first seven weeks. Interaction, discussion, presentations, report required.

Students' presentations, in-class comments, examinations, and written submissions contribute to the course evaluation. The term-project report is due at the last class meeting December 5, accepted the next day with no penalty. Items late after December 13 may get incomplete grade. Students should confer with the instructor in office hours about report content, papers read, etc., at least twice in the ten-week term.

The following materials Course Outline (and the book, journal list at the end) give a framework to individuals can adapt to their needs. Text references are TK - Theodoridis/Koutroumbas; DH - Duda/Hart; DHS -Duda/Hart/Stork.