# CSCE 768 Pattern Recognition and Classification

**Prerequisites:** STAT 509 (Statistics for Engineers) or
510 (Introduction to Applied Probability) or 511 (Probability)

**Meeting Time and Place:** TTh 930-1045, Sumwalt 241.

**Instructor:** Marco Valtorta

**Office:** Sumwalt 206A, 777-4879

**E-mail:**
mgv@cs.sc.edu

**Office Hours: Mon 3-5, Wed 3-5**,

**Grader:** No grader for this course

## Goals of the Course

The goals of this course, as adapted from the
old departmental syllabus, are:

- To introduce the field of pattern recognition, including feature
extraction, pattern classification, and cluster analysis.
- To describe the main statistical pattern recognition approaches and
algorithms.
- To present statistical decision theory.
- To describe linear and nonlinear classifiers, including neural
networks and their relation to Bayesian decision theory.
- To study the foundations of the theory of classifiers.
- To study issues of sample and time complexity in pattern classification or concept learning.

## Grading Policy

The precise grading policy has not yet been
determined.
The course grade will be based on homework, a midterm exam (or possibly two
midterm exams), a
final exam, possible quizzes. The homework consists of both paper and pencil
exercises and computer exercises using Maple.
A final project involves the analysis of a data set using a classifier such as
Parzen windows, the k-nearest-neighbors method, etc. While the final project
can be carried out using Maple, most students in the past found it
more convenient to implement the algorithm using a traditional imperative
programming language such as C.

## Code of Student Academic Responsibility

You are expected to be aware of and to follow the academic code of
responsibility that appears in the Carolina Community Student Policy
Manual.
Except for explicitly designated team assignments, all work that is turned
in is expected to be
your own.

Final Exam (take-home)

Oral Exam Schedule

Syllabus and Required Texts

Lecture Log

Here is the reference to another text on statistical pattern recognition:
Fukunaga, Keinosure. *Introduction to Statistical Pattern Recognition (second
edition)*. Academic Press, 1990. ISBN: 0-12-269851-7.

**Some useful links: **

A talk on hidden Markov models that emphasizes the
connection to Bayesian nets (in postscript).

How to view postscript in Windows:
Wim Sweldens's web page on GSview.

A handout with Maple examples
related to Exercise 14, Ch.2 DHS01.

An introduction to Maple:
Professors Miller and Meade's "Day 1" lecture.

A Talk by Kathy Laskey:
Bayesian Decision Theory and Machine Learning.