Kai Li

Kai Li

Researcher and educator working in statistics, reinforcement learning, modeling, and simulation.

Reading & Resources

Books have been an important part of my academic journey. This page collects texts and resources that have shaped my studies, served as useful references, or remained on my reading list. The editions shown are generally the ones I used or recorded at the time; this is a personal shelf rather than a ranking of the newest books.

Machine learning

  1. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R (Springer, 2017).
  2. Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. (Springer, 2016).
  3. Tom M. Mitchell, Machine Learning (McGraw-Hill, 1997).
  4. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, Foundations of Machine Learning, 2nd ed. (MIT Press, 2018).

Reinforcement learning

  1. Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, 2nd ed. (Bradford Books, 2018).

Deep learning

  1. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning (MIT Press, 2016).

R and statistical computing

Posit’s RStudio cheat sheets are useful quick references for R, RStudio, Quarto, and many common packages.

  1. Alain Zuur, Elena N. Ieno, and Erik Meesters, A Beginner’s Guide to R (Springer, 2009).
  2. Peter Dalgaard, Introductory Statistics with R, 2nd ed. (Springer, 2008).
  3. Garrett Grolemund, Hands-On Programming with R: Write Your Own Functions and Simulations (O’Reilly, 2014).
  4. Phil Spector, Data Manipulation with R (Springer, 2008).
  5. Owen Jones, Robert Maillardet, and Andrew Robinson, Introduction to Scientific Programming and Simulation Using R, 2nd ed. (Chapman & Hall/CRC, 2014).
  6. Maria L. Rizzo, Statistical Computing with R, 2nd ed. (Chapman & Hall/CRC, 2019).
  7. Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis, 2nd ed. (Springer, 2016).
  8. Yihui Xie, J. J. Allaire, and Garrett Grolemund, R Markdown: The Definitive Guide (Chapman & Hall/CRC, 2018).
  9. Yihui Xie, Christophe Dervieux, and Emily Riederer, R Markdown Cookbook (Chapman & Hall/CRC, 2020). This is a complementary, example-driven reference rather than a second edition of R Markdown: The Definitive Guide.
  10. Hadley Wickham, R Packages: Organize, Test, Document, and Share Your Code (O’Reilly, 2015).
  11. Stef van Buuren, Flexible Imputation of Missing Data, 2nd ed. (Chapman & Hall/CRC, 2021).
  12. Hadley Wickham, Advanced R, 2nd ed. (Chapman & Hall/CRC, 2019).

SAS

  1. Lora D. Delwiche and Susan J. Slaughter, The Little SAS Book: A Primer, 6th ed. (SAS Institute, 2019). A practical introduction to SAS.
  2. Ronald P. Cody and Jeffrey K. Smith, Applied Statistics and the SAS Programming Language, 5th ed. (Pearson, 2005).
  3. Maura E. Stokes, Charles S. Davis, and Gary G. Koch, Categorical Data Analysis Using SAS, 3rd ed. (SAS Institute, 2012).

Data science

  1. Hadley Wickham and Garrett Grolemund, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (O’Reilly, 2017).
  2. Wes McKinney, Python for Data Analysis: Data Wrangling with pandas, NumPy, and IPython, 2nd ed. (O’Reilly, 2017).
  3. Joel Grus, Data Science from Scratch: First Principles with Python, 2nd ed. (O’Reilly, 2019).
  4. Steven S. Skiena, The Data Science Design Manual (Springer, 2017).
  5. Sebastian Raschka and Vahid Mirjalili, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd ed. (Packt, 2019).
  6. Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd ed. (O’Reilly, 2022).
  7. Nate Silver, The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t (Penguin Press, 2012).