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:
- Advanced Machine Learning, (Spring semester).
- Organization of web information, (Spring semester).
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
- 30% Mid-semester exam
- 45% End semester exam
- 15% Homeworks
- 10% Two to three short quizzes
(best n-1 of n quizzes used for grading. There will be no compensation for missed quizzes except under very special circumstances.)
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