Machine Learning 2007

# Homework 1: due on June 13, 10 am

# Homework 2: due on July 20, 4 pm

# Homework 3: due on August 20, 6pm

# Finalize your project within 13^th July, give me a short

write up (not more than 1 page) within 13^th July. You can find

some project ideas here, but you are free to define your own.

Lecture Timings:

Day	Time
Wednesday	1000 hrs to 1200 hrs
Friday	1600 hrs to 1800 hrs

Books and References

Books:

1)  Machine Learning, Tom M. Mitchell, McGraw-Hill International Edition, 1997
2)  Pattern Classification, Duda Hart and Stork, Wiley 2000

Other related course websites:

1) Andrew Ng's  machine learning course (We shall use much of these course notes)
2) Pabitra Mitra's machine learning and knowledge discovery course.

Schedule (Tentative)

Class 1: May, 9	Introduction
Class 2: May, 11	Regression	Prof. A. Ng´s notes
Class 3: May, 16	Bayesian Learning	Duda Hart: Chapter 1 & 2 Mitchell Chapter 6 Prof. A. Ng´s notes
Class 4 : May, 18	Bayesian Learning
Class 5: May 25	Non Parametric Methods
Class 6: May 30	Neural Networks
Class 7: Jun 1	Neural Networks
Class 8: Jun 6	Support Vector Machines	Prof. A. Ng´s notes The SMO algorithm The simplified SMO
Class 9: Jun 13	Support Vector Machines
Class 10: Jun 15	Support Vector Machines
Jun 20	No Class
June 22
Class 11: Jun 27	Support Vector Machines
Class 13: Jun 29	Feature Selection Prof. Ngs notes
Class 14: July 4	Review
Class 15: July 6	Mid term test
Class 16: July 11	Test Review
Class 17: July 13	Principal Component Analysis Prof Ng´s notes Eigen Faces
Class 18: July 17	Fuzzy Sets Notes
Class 19: July 20	Fuzzy sets
Class 20: July 24	Neuro-Fuzzy Systems A paper on NF classification
Class 21: July 27	Expectation Maximization Prof Ngs Notes
Class 22: July 31	Minimum Description Length Mitchell Ch:6
Class 23: August 1	No Class
Class 24: August 3	Project Advances
Class 25: August 8	Learning Theory Prof Ngs notes
Class 26: August 10	Learning Theory
Class 27: August 15	Review
Class 28: August 17	Final Exam
Class 29: August 24	Project Presentations