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.
- 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
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006. website
Hastie, Tibshirani, Friedman
The elements of Statistical Learning
T. Mitchell. Machine Learning. McGraw-Hill, 1997.
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
Boyd and Vandenberghe
Convex optimization Book available online: Local copy
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