# Course to begin on the week starting at
Instructor: Debrup Chakraborty ( EMail:
debrup(at)delta.cs.cinvestav.mx)
Day 
Time 
Wednesday 
1600 hrs to 1800 hrs 
Friday 
1600 hrs to 1800 hrs 
This course
do not really have any prerequisites. Knowledge of basic probability theory and
linear algebra would be useful. All maths involved will be discussed in class.
This course will cover fundamental theory and
techniques involved in Pattern Recognition and Machine Learning. We also intend
to cover some recent research topics. The following broad categories will be
covered:
1) Introduction to Pattern Recognition and Learning Systems
2) Regression
2) Bayesian Learning
3) Nonparametric methods
4) Linear Discriminants and Support Vector Machines
5) Neural Networks
6) Decision Trees
7) Feature selection
8) Model selection
9) Introduction to learning theory
10) Unsupervised learning methods
11) Online active and reinforcement learning
1) Machine Learning, Tom M. Mitchell,
McGrawHill International Edition, 1997
2) Pattern Classification, Duda Hart and Stork, Wiley 2000
3) Introduction to Neural Networks, Simon Haykin, Prentice Hall, 1998
Relevant papers from current journals (to be
announced later)
1) Andrew
Ng's machine learning course
2) Pabitra Mitra's machine learning and knowledge
discovery course.
Schedule
Class 1: May, 24 
Introduction 


Class 2: May, 26 
Regression


Class 3: May, 31 
Bayesian Learning 
slides (in pdf) 

Class 4:
June, 2 
Bayesian Learning 
slides (in pdf) 

Class 5: June, 6 
Nonparametric Methods 


Class 6: June, 14 
Neural Networks 


Class 7: June, 16 
Neural Networks 


Class 8: June, 21 
Fuzzy Sets in Pattern Recognition 
Slides (in ppt) 
Homework 1 due 
Class 9: June 23 
Fuzzy Sets in Pattern Recognition 
Projects to be
finalized 

Class 10: June 28 
Support Vector Machines 


Class 11: June 30 
Support Vector Machines 


Class 12: July 7 
Support Vector Machines 


Class 13: July 12 
Mid term review 

Homework 2, due 
Class 14: July 14 
Test 1 


Class 15: July 19 
Principal Component Analysis 

Class 16: July 21 
Feature & Model Selection 


Class 17:
July 26 
Project review 


Class 18: July 28 
Learning Theory 


Class 29: Aug 2 
Learning Theory 

Homework 3, due 
Class 20:
Aug 4 
No class 


Class 21:
Aug 9 
Selected Topics (to be decided) 

Homework 4, due 
Class 22:
Aug 11 
Review 


Class 23: Aug 16 
Test 2 


Class 24: Aug 18 
Project Presentations 

