STAT-427/627 Statistical Machine Learning Lecture Notes

Preface

These lecture notes are the first draft for a course in Statistical Machine Learning using the 2nd version of An Introduction to Statistical Learning with Applications in R. (James et al., n.d.).

These notes are also based on lectures by Professor Michael Baron at American University for the same course. Any mistakes or points of confusion are completely due to me.

The notes will use the R Statistical programming language (2022) as well as the {tidyverse} package (Wickham et al. 2019) and examples using many other packages.

The course explores methods for creating models of reality based on data while navigating the complexities suggested in the following two statements.

We all know that art is not truth. Art is a lie that makes us realize truth, at least the truth that is given us to understand. The artist must know the manner whereby to convince others of the truthfulness of his lies.

— Picasso, Pablo (1923)

XKCD has a thought-provoking perspective we should be careful to remember as well.

Please send any corrections or recommendations to rressler@american.edu.

These notes were produced using Quarto from Posit. You can choose to read them in “dark mode” by using the toggle under the Title in the left margin.

James, Gareth, Daniela Witten, Trevor Hastie, and Rob Tibshirani. n.d. Introduction to Statistical Machine Learning with r. 2nd ed. Springer. https://hastie.su.domains/ISLR2/ISLRv2_website.pdf.

R Core Team. 2022. “R: A Language and Environment for Statistical Computing.” https://www.R-project.org/.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse” 4: 1686. https://doi.org/10.21105/joss.01686.