Pattern Recognition

This course follows the structure of the JNTUK syllabus for undergraduate students. In this course, we learn the fundamental concepts of pattern recognition along with the mathematical background. The content will cover Bayesian learning for pattern classification and Hidden Markov Models for speech applications. Most of the lecture notes are prepared from the NPTEL Course and Pattern Classification (Richard O. Duda) textbook.  The following content will be covered in the course:

1. Introduction to Bayesian Learning (Slides,  Lecture Notes)

2. Normal Density (Lecture Notes)   

3. Complete Lecture Notes (Unit-1, Unit-2)

4. Maximum Likelihood and Bayesian Parameter Estimation 

5. Unsupervised Learning and Clustering

6. Hidden Markov Models and Applications to Speech Processing