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
Prerequisites:
Programming (c, Matlab, Python), Basic Mathematics
Textbooks:

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

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

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

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

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.
Other Useful Links:

Machine Learning (Coursera)

Linear Algebra (MIT)

Introduction to Probability and Statistics

Basics of Statistical Machine Learning