Statistical foundations of machine learning
- 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 jeeves.cse.iitb.ac.in for announcements.
- Lecture calendar
- Helpful for revising before exams.
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
Grades are kept in a
- 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
- 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,
Regularization, Ridge and Lasso
- Support vector machines:
- Variants of the max-margin objective,
Ranking, Ordinal regression,
- Exponential loss,
Bagging, boosting, ECOC
- Spectral methods:
- Principal component analysis,
Singular value decomposition,
- Factor analysis:
- Non-negative matrix factorization,
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.