EE 575 - Statistical Inference and Modeling


  • The course is regularly co-taught by Jitkomut, Chanchai and Widhayakorn.
  • All homework assignments are accessible through CourseVille.

Lecture Notes

Available for download here.

  1. Introduction to Statistical Learning (JSS)
  2. Bayes Decision Theory (WAS)
  3. Parametric and Nonparametric Classification Techniques (WAS)
  4. Linear Regression (JSS)
  5. Logistic Regression (JSS)
  6. Regularization Techniques and Equivalences (JSS)
  7. Resampling Method (JSS)
  8. Expectation-Maximization Algorithm and Its Applications (JSS)
  9. Feature Extraction (PCA and LDA) (CPW)
  10. Feature Selection (CPW)
  11. Neural Networks: Multilayer Perceptron and Its Variants (WAS)
  12. Support Vector Machine (WAS)
  13. Unsupervised Learning (K-Mean) (WAS)
  14. Advanced Topics (Deep Learning, Reinforcement Learning) (WAS)

Course Information


Mon Wed 11 AM - 12:30 PM

  • Jitkomut Songsiri (JSS)
  • Assist. Prof. Charnchai Pluempitiwiriyawej (CPW)
  • Assist. Prof. Widhyakorn Asdornwised (WAS)
  • 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.