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Course Information
Identification

CS 725: Foundations of machine learning
 
Description

Remedial co-requisite: Mathematical foundations (Separately proposed by Prof. Saketh Nath)

Recommended parallel courses: CS709 (Convex optimization)

Course Content :

Supervised learning: decision trees, nearest neighbor classifiers, generative classifiers like naive Bayes, linear discriminate analysis, loss regularization framework for classification, Support vector Machines

Regression methods: least-square regression, kernel regression, regression trees

Unsupervised learning: k-means, hierarchical, EM, non-negative matrix factorization, rate distortion theory.
 
References

Most of the suggested readings will be freely available online. Here are some textbooks to refer to:

1.Understanding Machine Learning. Shai Shalev-Shwartz and Shai Ben-David. Cambridge University Press. 2017.
2. The Elements of Statistical Learning. Trevor Hastie, Robert Tibshirani and Jerome Friedman. Second Edition. 2009.
3. Pattern Recognition and Machine Learning. Christopher Bishop. Springer. 2006.
4. Foundations of Data Science. Avrim Blum, John Hopcroft and Ravindran Kannan. January 2017.
 
Home Page

https://www.cse.iitb.ac.in/~sunita/cs725/index.html
 
Prerequisites

N/A
 
Other Details

Duration : Full Semester Total Credit : 6
Type : Theory
 
Autumn Semester 2019-20

Status : Offered Instructor : Prof. Sunita Sarawagi
 
Spring Semester 2019-20

Status : Not Offered Instructor : ---




Last Modified Date: 15-Jul-2013

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