Statistical foundations of machine learning
Autumn 2006

Administrivia

Instructor

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 jeeves.cse.iitb.ac.in 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]

Assignments

To be worked out in small groups of 2--3 students. [1 2 3]

Evaluation

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.

Resources

Primary books

• The Elements of Statistical Learning by Hastie, Tibshirani, Friedman
• Pattern Classification, 2nd ed., by Duda, Hart and Stork, Wiley-Interscience
• Convex Optimization by Stephen Boyd and Lieven Vandenberghe
• Machine Learning by Tom Mitchell

Supplementary books

• An Introduction to Probability Theory by Feller
• All of Statistics by Larry Wasserman
• Principles of Data Mining by Hand, Mannilla, Smyth

Additional reading

• Andrew Moore's lecture slides
• Numerical Recipes in C, online book

Software and manuals

• used in class.
• Download Scilab, a free alternative to Matlab. (To save time and bandwidth, get it internally from IIT's FTP server.) Here is help to migrate from Matlab to Scilab.
• Or you can use R. Here is a manual.
• Internal link to download Matlab; we have limited license seats so try to migrate to Scilab or R.
• David Madigan's Bayesian logistic regression software.
  

Eligibility

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.