Pattern Recognition

Spring 2006

1. Goal of Course

Pattern recognition can be broadly defined as a process to generate a meaningful description of data and deeper understanding of a problem through manipulation of a larger set of primitive raw data. More specifically we will be answering question: “What is this?

2. Recommended Textbook

Pattern classification, Second Edition, Duda, Hart and Stark

3. Instructor

[more details]

Name

Dr. M Asmat Ullah Khan.  (Pakistan)

Contact Information

4. Tentative Schedule (tentative)

Kind

Title

Lecture 1

Introduction, Fundamental steps in Pattern recognition.

Lecture 2

Probability and Random Variables.

Lecture 3

Random Vectors, Co-variance matrices.

Lecture 4

Vector Spaces.

Lecture 5

Linear Transformations.

Lecture 6

Eigen-values and Eigen Vectors.

Lecture 7

Introduction to decision theory, Likelihood ratio, Bayes risk.

Lecture 8

MAP, MAL criterion, Discriminant Functions.

Lecture 9

Dimentionality reduction and PCA.

Lecture 10

LDA vs. PCA, LDA variants.

Lecture 11

Linear and quadratic classifier part1.

Lecture 12

Linear and quadratic classifier part2.

Lecture 13

The K-nearest neighbor rule.

Lecture 14

Density estimitaion.

Lecture 15

Validation part1.

Lecture 16

Validation part2.

Exam

Project Presentation

5. Grading Policy

There will be one long project during the semester for the course. The project will be evaluated by a written report and presentation.

6. Download the syllabus file (MS-WORD) :      [DOWN]