Statistical foundations of machine learning
Autumn 2005


Soumen Chakrabarti
Teaching assistant
Kriti Puniyani,
Time and place
Slot 13, Mon+Thu 6:30--8pm, 301 SIT
Course newsgroup
Watch cse.cs705 (internal access) on for announcements.
Lecture calendar
Helpful for revising before exams.
Internal link to upload solutions to assignments.


Please complete and submit the first student evaluation to the TA.

Credit students will need to write a midterm, a few peer-evaluated surprise quizzes and a final, and do a few assignments (typically using Scilab, WEKA, and perhaps Java).

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.


Primary books
Supplementary books
Additional reading
Software and manuals

Tentative syllabus

Basic vector algebra, calculus prob/stat, Linear and non-linear optimization
Bayesian learning:
Bayes optimal error, parametric models, parameter estimation and smoothing, EM
Regression and classification:
Loss functions, Linear regression and discriminants, Local regression and kernels, Logistic regression, Bias-variance tradeoff, Regularization, Ridge and Lasso
Support vector machines:
Variants of the max-margin objective, Optimization techniques, Structure prediction, Ranking, Ordinal regression, Transductive learning
Exponential loss, Bagging, boosting, ECOC
Spectral methods:
Principal component analysis, Singular value decomposition, Graph partitioning
Factor analysis:
Non-negative matrix factorization, Information bottleneck, Co-clustering and cross-associations
Density estimation (tentative):
Kernel density methods, Basis function and wavelets
Graphical models (tentative):
Directed Bayesian belief networks, Markov random networks, probabilistic relational models


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.

If you have already taken CS610, you will find quite some overlap. We are in a transition year: next year onwards, CS610 will leave out all CS705 material and specialize more on text, Web, and XML applications.

CS610 in Spring 2006 will not list CS705 as a prerequisite. However, you may be at a disadvantage if you cannot master on your own the Fall 2005 material.

Mtech2s who have taken CS610 in Spring 2005 are discouraged from taking the course on credit.

There will be slight (about 2--3 lectures at most) overlap with IT655.