Statistical foundations of machine learning
Autumn 2008

Administrivia

Instructor

Soumen Chakrabarti

Teaching assistants

Avinava Dubey , Jinesh Machchhar

Time and place

As per the latest timetable, we are in slot 8, i.e., Mon,Thu 2:00--3:25 in classroom SIC301 (tentative).

Moodle

Visit the Moodle site for CS705 here.

Syllabus and lecture calendar

Material covered with links to papers and notes.

Course newsgroup

Watch cse.cs705 (internal access) on jeeves.cse.iitb.ac.in for announcements.

Homework assignments

Instead of batched homeworks we will offer one ever-growing log file of homeworks with a separate due date for each problem. To be worked out in small groups of 2--3 students.

Evaluation

Credit students will need to write a midterm (40 marks) and a final (45 marks), and do a few assignments, typically using Scilab, WEKA, and perhaps Java (25 marks). The total is 110, which will be scaled to 100.

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 out as of 2008/12/05, with detailed scores, curve and cutoffs.

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
• Some additional papers and links

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.

CS635 in Spring 2009 will list CS705 as a prerequisite.