CS 725: Foundations of Machine Learning (Autumn 2009)

Lecture Schedule Slot 5, Wednesdays, Fridays: 9:30--11am.
Venue SIC 301, KR Building, CSE Department.
Instructor: Sunita Sarawagi
TA: Rakesh Pimplikar and Anandraj
Instructor's office hours check here
Syllabus and week-wise calendar: Click here
Homeworks Click here
Previous years' exams

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 optimization and Mathematical Foundations 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:

Advanced Machine Learning, (Spring semester).
Organization of web information, (Spring semester).
CS 635: Web search and mining, (co-requisite for the Autumn offering).

Eligibility

The course is open to CS MTechs, PhD, DD and BTech students. 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

20% Mid-semester exam
40% End semester exam
25% Homeworks
15% Three short surprise quizzes (best two of three quizzes used for grading. All quizzes will be surprise and there will be no compensation for missed quizzes except under very special circumstances.)
Audit students have to score more than 30% over all, including assignments, quizzes and mid/end sem exam.
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.

[ 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 readings
Lecture notes from some of the previous offerings of the class.