Statistical foundations of machine learning
Please report any dead links to me.
- Soumen Chakrabarti
- Teaching assistants
- Avinava Dubey
- Time and place
- As per the
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 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.
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
Grades are out as of 2008/12/05, with detailed scores, curve and cutoffs.
- Primary books
- 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
- 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.