Pattern Recognition and Machine Learning.

Announcements:

# Project reports due on August 17

# Home work 4, due on August 9, 2006 before 4:00 PM

# Home work 3, due on August 2, 2006 before 4:00 PM

# Home work 2, due on July 12, 2006 before 4:00 PM

# You can find some projet ideas here. Finalize your project within June, 23.

# No class on Friday, 9^th June.

# Home work 1, due on June 21, 2006 before 4:00 PM

# Course to begin on the week starting at 22nd May, 2006 (2 weeks later than the usual schedule)

Instructor: Debrup Chakraborty ( EMail: debrup(at)delta.cs.cinvestav.mx)

Lecture Timings:

Day	Time
Wednesday	1600 hrs to 1800 hrs
Friday	1600 hrs to 1800 hrs

Prerequisites

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.

Course Contents (Tentative)

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) Non-parametric 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

Books and References

Books:

1)  Machine Learning, Tom M. Mitchell, McGraw-Hill International Edition, 1997
2)   Pattern Classification, Duda Hart and Stork, Wiley 2000
3)  Introduction to Neural Networks, Simon Haykin, Prentice Hall, 1998

Papers:

Relevant papers from current journals (to be announced later)

Other related course websites:

1) Andrew Ng's  machine learning course
2) Pabitra Mitra's machine learning and knowledge discovery course.

Schedule

Class 1: May, 24	Introduction	slides (in ppt)
Class 2: May, 26	Regression	slides (in pdf),	Prof. A. Ng´s notes
Class 3: May, 31	Bayesian Learning	slides (in pdf)
Class 4: June, 2	Bayesian Learning	slides (in pdf)
Class 5: June, 6	Nonparametric Methods	(slides in ppt)
Class 6: June, 14	Neural Networks	slides in ppt
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	A paper on NF classification	Projects to be finalized
Class 10: June 28	Support Vector Machines	Prof Ng’s notes
Class 11: June 30	Support Vector Machines	Burges Tutorial
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	Slides (in pdf)	Prof Ng´s notes
Class 16: July 21	Feature & Model Selection
Class 17: July 26	Project review
Class 18: July 28	Learning Theory		Prof Ng´s notes
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