Textbook and Reference Books

Course Textbook(s):

• Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning - with Applications in R,  Springer, 2013 (Corrected at 8th printing 2017) [Download].
• Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning - with Applications in R, 2nd ed.,  Springer, 2021 [Download] (or you may download the book and other materials from its website).
• Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009 (Corrected at 12th printing 2017) [Download].

Supplementary Books and Materials:

• Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012 [Download].
• Peter Harrington, Machine Learning in Action, Manning Publications Co.,  2012 [Download].
• Charu C. Aggarwal, Neural Networks and Deep Learning: A Textbook, Springer, 2018 [Download].
• G. Jay Kerns, Introduction to Probability and Statistics Using R, 1st ed., 2015 [Download].
• Programming with R (online at https://swcarpentry.github.io/r-novice-inflammation/).
• An R Introduction to Statistics at https://www.r-tutor.com/r-introduction.
• Elementary Statistics with R (online at https://www.r-tutor.com/elementary-statistics).
• Robert V. Hogg, Elliot A. Tanis, Dale Zimmerman, Probability and Statistical Inference, 9th ed., 2015 [Download].
• Larry Wasserman, All of Statistics - A Concise Course in Statistical Inference, 2004 [Download].
  
• An Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics, W. N. Venables, D. M. Smith, and the R Core Team, Version 4.1.1 (2021-08-10), (you may download it here, or search the web).

Downloads on this page may be on other ODTUClass sites!
Thus, they may not work properly!