Instructor: Ganesh Ramakrishnan
TAs: Amrita Saha, Kedharnath Narahari, Subhabrata Mukherjee, Ajay Nagesh
TA Contacts
Amrita Saha - amrita@cse.iitb.ac.in - 9820700957
Kedharnath Narahari - kedhar@cse.iitb.ac.in - 9757059649
Subabrata Mukherjee - subhabratam@cse.iitb.ac.in - 9322013946
Ajay Nagesh - ajaynagesh@cse.iitb.ac.in - 9920470808
Calendar
List of applets
Sample papers for reading
Calendar (from previous offering)
Class Timings, Venue and Grading
Time: Tuesdays and Fridays, 2:00-3:30 PM
Venue: SIC 301, KReSIT
Office hours (for doubt clarifications): Thursday, 5:00-6:30 PM
Credit/Audit Requirements Approximate credit structure20% Mid-semester exam 40% End semester exam 25% Homeworks 15% Three quizzes (best two of three quizzes used for grading.) Audit students have to score more than 40% over all, including assignments, quizzes and mid sem exam. End-sem exam is optional for audit students.
Prerequisites
An upper-level undergraduate course(s) in algorithms and data
structures is mandatory, whereas a basic course on probability and statistics and some basic
understanding of linear algebra are desirable, but not mandatory. 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, (weak 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.
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]
- Duda, Hart and Stork. Pattern Classification (2nd Edition). Wiley, 2000.
Supplementary books
- [ PRS ]
-
- Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
- An Introduction to Probability Theory and Its Applications, Vol. 1, William Feller, 3rd edition, Wiley International
- Introduction to Probability Theory, Paul. G. Hoel, Sidney Port, Charles Stone
- [ BCAO]
- D. P. Bertsekas Convex Analysis and Optimization, Athena Scientific, 2003 [ BV ]
- Boyd and Vandenberghe Convex optimization Book available online
- [ Han00]
- Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers
Slides by Tom Mitchell
Lecture Notes
Exercises
Programs
Solutions
