Statistical foundations of machine learning
Autumn 2006

Please report any dead links to me.


Soumen Chakrabarti
Teaching assistant
Sunkari Sasidhar,
Time and place
Only 2006/07/27: 6:30--8pm, SIC301. Thereafter: slot 12 (Mon+Thu 5:00--6:30pm) SIC301.
Course newsgroup
Watch cse.cs705 (internal access) on for announcements.
Syllabus and lecture calendar
Helpful for revising before exams. Also has a tentative syllabus.
Short problems to work out
Soumitra Pal has volunteered to put together lists of short exercises based on results I quote in class and claim that you can verify offlne. Thanks, Soumitra! [1 2 3 4 5 6]
To be worked out in small groups of 2--3 students. [1 2 3]


Credit students will need to write a midterm (30%), possibly a few peer-evaluated surprise quizzes (none materialized) and a final (45%), and do a few assignments, typically using Scilab, WEKA, and perhaps Java (25%).

Audit students have to write only the final exam and, to pass, their score must be above the bottom 20 percent of the class in finals only.

Grades are kept in a password-protected area. Grades to be frozen 2006/12/08 midday!

When requested, please complete and submit the first student evaluation to the TA.


Primary books
Supplementary books
Additional reading
Software and manuals


Mtech1 and DD4s who have not yet taken CS610 are the primary targets for this course. But it is open to all PG, DD and Btech4 students of all departments subject to department/facad approval. If you are CSE Btech3, ask for my consent by email while citing your CPI and grades in Paradigms and Prob/Stat.

This course should be reasonably simple if you have a little background in vector algebra, calculus, and probability. Some basic algorithms background may also help.

CS610 in Spring 2007 will list CS705 in Autumn 2006 as a prerequisite.