Basic Statistical Learning: a Guide for the Perplexed

Preface

Before we begin: note that this book (including this preface!) is under active development.

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The development of this textbook grew out of a need to support non-statistics students at Carnegie Mellon University in their data analysis efforts.

For a deeper treatment of the topics in this book, see, e.g., James et al. (2021) and James et al. (2023), fantastic textbooks that should be part of any analyst’s (digital) bookshelf!.

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As far as the subtitle: late in the 12th century, the Jewish theologian Moses Maimonides wrote a work that, in the words of Wikipedia, aimed “to reconcile Aristotelianism with Rabbinical Jewish theology by finding rational explanations for many events…” We won’t be doing this here, mind you. We’re just going to borrow the title, because it is very apropos for describing the feelings of domain specialists who are approaching statistical learning for the first time. Or even for the second time. Or the third…

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in r. Springer New York, NY. https://doi.org/10.1007/978-1-0716-1418-1.

James, Gareth, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. 2023. An Introduction to Statistical Learning: With Applications in Python. Springer New York, NY. https://doi.org/10.1007/978-3-031-38747-0.