CS 419: Introduction to Machine Learning (Autumn 2011)

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.

Prerequisites

• Data Structures and Algorithms (CS 213)
• Data Analysis and Interpretation (IC 102) or equivalent as taught by the respective departments.

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

Approximate credit structure (Subject to change)

• 25% Mid-semester exam
• 45% End semester exam
• 20% Homeworks
• 10% In class quizzes

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

[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