EE 575 - Statistical Inference and Modeling
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Announcement
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- The course is regularly co-taught by Jitkomut, Chanchai and Widhayakorn. 
- All homework assignments are accessible through CourseVille.


Lecture Notes
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Available for download here. More handouts will be added.

1. `Introduction to Statistical Learning`_ 
#. `Linear Regression`_ 
#. Robust regression
#. `Resampling Method`_
#. Model selection
#. Bootstrap method
#. `Regularization techniques`_
#. Nonlinear regression model
#. Overview of classification (Bayes' theorem, performance indices)
#. `Logistic Regression`_
#. k-nearest neighbor
#. Linear and quadratic discrimination analysis (LDA, QDA)
#. Support vector machine
#. Tree-based methods and ensemble models
#. Overview of unsupervised learning 
#. Principle component analysis (PCA)
#. `Gaussin mixture models (GMM) and EM algorithm`_ 
#. Self-orgnaizing maps (SOM)

.. _Introduction to Statistical Learning: ./ee575/intro.pdf
.. _Linear Regression: ./ee575/linreg.pdf
.. _Logistic Regression: ./ee575/logreg.pdf
.. _Regularization techniques: ./ee575/reg.pdf
.. _Resampling Method: ./ee575/resamp.pdf
.. _Gaussin mixture models (GMM) and EM algorithm: ./ee575/em.pdf

Course Information
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:Lectures: 
	Mon Wed 11 AM - 12:30 PM 

:Instructors:
	- Jitkomut Songsiri (JSS)
	- Assist. Prof. Charnchai Pluempitiwiriyawej (CPW) 
	- Assist. Prof. Widhyakorn Asdornwised (WAS)

:Textbooks: 

	- Jerome H. Friedman, *Robert Tibshirani, and Trevor Hastie, The Elements of Statistical Learning*, 2nd Edition, Springer
	- Daniela Witten, Gareth James, Robert Tibshirani, and Trevor Hastie, *An Introduction to Statistical Learning: With Applications in R*, Springer, 2013
	- Richard O. Duda, Peter E. Hart, and David G. Stork, *Pattern Classification*, John Wiley & Sons, 2001.