CS 725: Foundations of Machine Learning (Autumn 2013)


Lecture Schedule Slot 11, Tuesdays, Fridays: 3:30--5pm.
Venue LCH31, Lecture Hall Complex
Instructor: Sunita Sarawagi

Course email: cs725.iitb [at] gmail [dot] com
TAs: S V Vivek, Rahul Mitra, Jayaprakash S, Abhirut Gupta, Aditya S., Bhushan K.
Instructor's office hours check here
Syllabus and week-wise calendar: Click here
Homeworks Click here
Course announcement, internal notes : moodle.iitb.ac.in

Prerequisites

An upper-level undergraduate course(s) in algorithms and data structures, a basic course on probability and statistics, basic understanding of linear algebra. This is a first course on machine learning and no prior knowledge of machine learning is assumed. You are urged to consider taking courses on Convex optimizatio running in parallel if you lack these background. Homework assignments will require programming in Java.

Post-requisites

This course is a prerequisite for the following courses:

Eligibility

The course is open to CS MTechs, PhD, DD and BTech students with CPI above 6.5. Students of other departments should approach for permission only if they meet the necessary pre-requisites. Third year BTech students need to take prior permission from the instructor for enrolling in the course.

Credit/Audit Requirements

Approximate credit structure Based on the limited TA support, we are forced to use the following policy for assigning pass/no-pass grades to audit students. We will *not* grade any assignments or exams of audit students. Instead, audit students will get a pass if they attend at least 90% of the classes.

Reading material

There is no single text book for the course. For each topic, we will list the relevant chapters from various books and papers.

Primary books

[ Bis07]
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006.
[ HTF]
Hastie, Tibshirani, Friedman The elements of Statistical Learning Springer Verlag
[Mit97]
T. Mitchell. Machine Learning. McGraw-Hill, 1997.

Supplementary books

[ PRS ]
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
[ GS ]
Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.
[ BV ]
Boyd and Vandenberghe Convex optimization Book available online: Local copy
[ Han00]
Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers
[JD88]
A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988. Local copy

Other useful resources