Instructor: Ganesh Ramakrishnan
TAs: Contact at cs725tas@googlegroups.com
TA Contacts
cs725tas@googlegroups.com
Video recordings and all course notes are all being made available on the LMS system.
Calendar, with notes etc will redirect to the LMS system for the latest slides
List of applets
Sample papers for reading
Course Description
CS 403/725 provides a broad introduction to machine learning and various fields of application. The course is designed in a way to build up from root level.
Topics include:
 Supervised Classification (perceptron, support vector machine, loss functions, kernels, neural networks and deep learning)
 Supervised Regression (Least square regression, bayes linear regression)
 Unsupervised classification (clustering, expectation maximization)
 Introduction to learning theory (bias/variance tradeoffs).
The course will discuss the application of machine learning in devanagari script recognition which is a developing field in the machine learning community.
Class Timings, Venue and Grading
Time: Tuesdays and Fridays, 5:307:00 PM, Discussion to Solutions of Tutorial from previous week: 5 PM  5:30 PM on Tuesdays
Venue: LA101
Office hours (for doubt clarifications): Tuesdays and Fridays 12:30 PM to 5 PM at my office SIA418, Fourth Floor, KReSIT
Credit/Audit Requirements15% Midsemester exam 30% End semester exam 15% 2 Quizzes 20% 2 Assignments + Inclass participation 20% Project No auditing is allowed this semester. Else, in the past, audit students had to submit assignments and project.
Prerequisites
 Basics of computer science including algorithms, data structure, complexity analysis etc.
 Basic Linear Algebra (course equivalent to MA 108 for BTech)
 Basic Probability Theory (course equivalent to CS 215 for BTech)
Eligibility
The course is open to all. Courses in Linear Algebra, Probability Theory will be helpful but not manadatory.
Primary books
[ HTF] (Main reference) Hastie, Tibshirani, Friedman The elements of Statistical Learning Springer Verlag
 [ Bis07]
 Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006.
 Tom Mitchell, Machine Learning. McGrawHill, 1997
 Kevin Murphy, Statistical Machine Learning
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
