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Course Information
Identification

CS 726: Advanced machine learning
 
Description

Machine learning theory: Generalization bounds, Active learning.

Graphical models: Representation and semantics, Undirected graphical models including Markov Random Fields, Conditional Random Fields Directed graphical models aka Bayesian networks.

Structured prediction tasks: overview and examples Inference algorithms: Message passing, Junction trees, Approximate inference,

Sampling: MCMC and Gibbs.

Learning: Completely and partially observed models, Parameter estimation via maximum likelihood, Bayesian and Max-margin methods, graph Structure learning.
 
References

1. Probabilistic Graphical Models: Principles and Techniques, by Daphne Koller and Nir Friedman, MIT Press, 2009.

2. Selected papers.
 
Home Page

http://www.cse.iitb.ac.in/~sunita/cs726
 
Prerequisites

Foundations of Machine Learning or equivalent
 
Other Details

Duration : Full Semester Total Credit : 6
Type : Theory
 
Current Semester (Autumn 2017-18)

Status : Not Offered Instructor : ---
 
Next Semester (Spring 2017-18)

Status : Offered Instructor : Prof. Sunita Sarawagi




Last Modified Date: 09-May-2016

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