CS 419: Introduction to Machine Learning (Autumn 2011)

Lecture Schedule Slot 5, Wednesday, Friday: 9:30--10:55.
Venue SIC 301, KR Building, CSE Department.
Instructor: Sunita Sarawagi
TA: Abhinav Maurya (ahmaurya@cse), Ms. Sudha Babanrao Bhingardive (sudha@cse)
Instructor's office hours check here
Syllabus and week-wise calendar: Click here
Homeworks Click here


This course will provide a broad overview of Machine Learning with a stress on applications.

Supervised learning: Decision trees, Nearest neighbor classifiers, Generative classifiers like naive Bayes, Support vector Machines

Unsupervised learning: K-Means clustering, Hierarchical clustering, EM, Itemset mining

Applications: image recognition, speech recognition, text and web data retrieval, bio-informatics, commercial data mining.



This course is part of the Computer Science minor for non-CSE undergraduates. Eligibility is as per institute norms for minors.

Credit/Audit Requirements

Approximate credit structure (Subject to change)

Reading List

Primary books

[ Bis07]
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006. website
[ HTF]
Hastie, Tibshirani, Friedman The elements of Statistical Learning Springer Verlag
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
A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988. Local copy