Patten Recognition


This course provides an introduction to pattern recognition, starting from the basics of linear algebra, statistics to a discussion on the advanced concepts as employed in the current research of pattern recognition. The course consists of a traditional lecture component supported by home works & programming assignments. There is one final exam.

Schedule:

         One lectures per week on Friday at 10:30am.
Classes to be held at CSCR seminar room

Pre-requisites: Programming (c, Matlab, Python), Basic Mathematics

Textbooks:

  1. Pattern Recognition (4th Edition), 2008 by Sergios Theodoridis and Konstantinos Koutroumbas  

  2. Data Mining: Concepts and Techniques (2nd Edition), 2006  by Jiawei Han and Micheline Kamber  

  3. Introduction to Linear Algebra  (4rd Edition), 2009  by Gilbert Strang  

  4. Introduction to the Theory of Statistics  (3rd Edition), 1974  by Alexander M. Mood, Franklin A. Graybill and Duane C. Boes 

  5. Fundamentals of Mathematical Statistics  (11th Edition), 2014  by S. C. Gupta and V. K. Kapoor 

Material:
          
Many of these slides are copied from various sources. Due acknowledgement to the wonderful Professors who have kept the slides on their web.
Topic Lecture Slides Additional Readings and Notes
Basics of Linear Algebra Slides Homework 1
Basics of Probability and Statistics Slides Homework 2, Homework 3
Clustering Analysis Slides Assignment1, PAM, CLARA and CLARANS
Feature Selection Slides
Feature Extraction Slides

Other Useful Links:

  1. Machine Learning (Coursera)

  2. Linear Algebra (MIT)

  3. Introduction to Probability and Statistics

  4. Basics of Statistical Machine Learning