Statistical foundations of machine learning
Autumn 2008

Please report any dead links to me.


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).
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 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.


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.


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.

CS635 in Spring 2009 will list CS705 as a prerequisite.