Kai Li

Kai Li

Statistics Ph.D. Student at Stony Brook University

Research

Current Research: Applications in Reinforcement Learning

Books that I love to read and recommend: (still updating!)

Machine Learning

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

Reinforcement Learning

  1. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, 2nd edition, 2018, A Bradford Book

Deep Learning

  1. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016, The MIT Press

R

See R cheatsheets for R packages, RStudio IDE, R Markdown and a lot more on https://www.rstudio.com/resources/cheatsheets/.

  1. A Beginner’s Guide to R by Alain Zuur, Elena N. Ieno and Erik Meesters, 2009, Springer
    • A good introduction to R without statistics.
  2. Introductory Statistics with R by Peter Dalgaard, 2nd edition, 2008, Springer
    • An excellent introduction to elementary data analysis in R.
  3. Hands-On Programming with R: Write Your Own Functions and Simulations by Garrett Grolemund, 2014, O’Reilly
    • A complete introduction to R with illustrations in real-world applications.
  4. Data Manipulation with R by Phil Spector, 2008, Springer
    • A very complete and detailed book in data manipulation in R.
  5. Introduction to Scientific Programming and Simulation Using R by Owen Jones, Robert Maillardet and Andrew Robinson, 2nd edition, 2014, Chapman and Hall/CRC
    • A very good overview of scientific computing, numerical analysis, optimization, systems of ODEs, Markov chains, and simulation in R.
  6. Statistical Computing with R by Maria L. Rizzo, 2nd edition, 2019, Chapman & Hall/CRC
    • An excellent introduction to statistical computing, simulation, and optimization in R.
  7. ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham, 2nd edition, 2016, Springer
    • An excellent comprehensive guide to ggplot2 in R. ggplot2 is well known for being a Swiss army knife for data visualization in R.
  8. R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire and Garrett Grolemund, 2018, Chapman and Hall/CRC
    • Excellent book on R Markdown. This is the book to read for R Markdown.
  9. R Markdown Cookbook by Yihui Xie, Christophe Dervieux and Emily Riederer, 2020, Chapman & Hall/CRC
    • The “second edition” of R Markdown: The Definitive Guide.
  10. R Packages: Organize, Test, Document, and Share Your Code by Hadley Wickham, 2015, O’Reilly
    • A complete guide to prepare for a new R package from scratch. This is the book to read for R packages.
  11. Flexible Imputation of Missing Data by Stef van Buuren, 2nd edition, 2021, Chapman and Hall/CRC
    • A detailed and technical book on imputation methods using R.
  12. Advanced R by Hadley Wickham, 2nd edition, 2019, Chapman and Hall/CRC

SAS®

  1. The Little SAS® Book: A Primer by Lora D. Delwiche and Susan J. Slaughter, 6th edition, 2019, SAS Institute
    • The SAS Bible for SASor: a thorough introduction to SAS.
  2. Applied Statistics and the SAS® Programming Language by Ronald P. Cody and Jeffrey K. Smith, 5th edition, 2005, Pearson
    • An excellent introduction to elementary data analysis in SAS®.
  3. Categorical Data Analysis Using SAS® by Maura E. Stokes, Charles S. Davis and Gary G. Koch, 3rd edition, 2012, SAS Institute

Data Science

  1. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund, 2017, O’Reilly
    • An excellent introduction to data science in R, including ggplot2, dplyr, tibble, readr, stringr, forcats, lubridate, magrittr, purrr, modelr, broom, and R Markdown.
  2. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney, 2nd edition, 2017, O’Reilly
    • An excellent introduction to data science in Python, including NumPy, pandas, matplotlib, seaborn, statsmodels, and scikit-learn using IPython and Jupyter notebook.
  3. Data Science from Scratch: First Principles with Python by Joel Grus, 2nd edition, 2019, O’Reilly
    • A comprehensive introduction to data science in Python with a more emphasis on machine learning.
  4. The Data Science Design Manual by Steven S. Skiena, 2017, Springer
    • A very comprehensive and easy-to-read introduction to data science for beginners.
  5. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 by Sebastian Raschka and Vahid Mirjalili, 3rd edition, 2019, Packt Publishing
  6. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron, 3rd edition, 2022, O’Reilly
  7. The Signal and the Noise: Why so many predictions fail but some don’t by Nate Silver, 2012, Penguin Press