EE 575 - Statistical Inference and Modeling

Announcement

  • The course is regularly co-taught by Jitkomut and Charnchai.
  • All homework assignments are accessible through CourseVille/MS Team.

Lecture Notes

Available for download here. More handouts will be added soon.

  1. Introduction to Statistical Learning
  2. Linear Regression
  3. Robust regression
  4. Model selection and cross validation
  5. Resampling Method
  6. Regularization techniques
  7. Nonlinear regression model
    • regression splinse
    • generalized additive models
    • feedforward neural networks
  8. Overview of classification
    • Logistic regression
    • Bayes decision theory
    • kNN, discriminant analysis (LDA,QDA)
    • classification performance indices
    • brief overview of other classification methods
  9. Support vector machine
  10. Tree-based methods and ensemble models
  11. Overview of unsupervised learning
  12. Principle component analysis (PCA)
  13. Gaussin mixture models (GMM) and EM algorithm
  14. Self-orgnaizing maps (SOM)

Lecture videos

Students can watch videos from my YouTube playlists on

  1. Regression and regularization in statistical learning (11 hours in English)
  2. Introduction to classification methods (6 hours in Thai)

Course Information

Lectures:

Mon Wed 11 AM - 12:30 PM
Instructors:
  • Jitkomut Songsiri (JSS)
  • Charnchai Pluempitiwiriyawej (CPW)
Textbooks:

The first two books in statistical learning (ISLR, ESL) are the main books of this course. Further reading in this field can be checked out from other books in the following list.

Statistical learning

  1. Daniela Witten, Gareth James, Robert Tibshirani, and Trevor Hastie, An Introduction to Statistical Learning: With Applications in R, Springer, 2013
  2. Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, The Elements of Statistical Learning, 2nd Edition, Springer

Machine learning, Pattern recognition

  1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
  2. Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012
  3. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, The MIT Press, 2016
  4. Tom .M. Mitchell, Machine Learning, McGraw-Hill, 1997
  5. Ethem Alpaydin, Introduction to Machine Learning, 2nd edition, 2010
      1. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, John Wiley & Sons, 2001.