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Subject:
From:
Tammie Dudley <[log in to unmask]>
Reply To:
PhD Student <[log in to unmask]>
Date:
Fri, 24 Apr 2009 14:04:13 -0400
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Summer 2009 CSc8980 Topics in CS: Statistical Learning Tools

Syllabus:
This is 4 CH class will be devoted to statistical learning tools
applied to mostly bioinformatics.
The book for the class is T. Hastie, R. Tibshirani, J. Friedman
The Elements of Statistical Learning (2nd ed) Springer 2009.
The topics include:
-  Linear Regression and Classification
   (Lasso, Logistic regression) 
-  Model Assessment and Selection
   (VC-dimension, Cross-validation, Bootstrap)
-  Model inference
   (Maximum Likelihood, Bayesian inference, EM, MCMC)
- Unsupervised Learning
   (Clustering, Self-organizing Maps, Principal Component
   Analysis, Non-negative Matrix Factorization)
- Additionally: Markov Graphs,

The appropriate methods should be applied to students'
research work with results reported in class.







-- 
Dr. Alexander Zelikovsky
Associate Professor
Department of Computer Science
Georgia State University
Phone: (404) 413 5730
Fax: (404) 413 5717
http://www.cs.gsu.edu/~cscazz <http://www.cs.gsu.edu/%7Ecscazz>

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