Kind

Title

Lecture 1

Introduction, Fundamental steps in Pattern recognition.

Lecture 2

Probability and Random Variables.

Lecture 3

Random Vectors, Covariance matrices.

Lecture 4

Vector Spaces.

Lecture 5

Linear Transformations.

Lecture 6

Eigenvalues 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 Knearest neighbor rule.

Lecture 14

Density estimitaion.

Lecture 15

Validation part1.

Lecture 16

Validation part2.

Exam

Project Presentation
