Lightweight Machine Learning Classics with R
DRAFT v0.2.3 2022-06-11 20:31 (b21f8e0)
These lecture notes are distributed in the hope that they will be useful. Any bug reports are appreciated.
Copyright (C) 2020-2022, Marek Gagolewski.
This material is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).
You can access this textbook at:
- https://lmlcr.gagolewski.com/ (a browser-friendly version)
- https://lmlcr.gagolewski.com/lmlcr.pdf (PDF)
- https://github.com/gagolews/lmlcr (source code)
Aims and Scope
Machine learning has numerous exciting real-world applications, including stock market prediction, speech recognition, computer-aided medical diagnosis, content and product recommendation, anomaly detection in security camera footage, game playing, autonomous vehicle operation, and many others.
In this book we will take an unpretentious glance at the most fundamental algorithms that have stood the test of time and which form the basis for state-of-the-art solutions of modern AI, which is principally (big) data-driven. We will learn how to use the R language (R Development Core Team 2021) for implementing various stages of data processing and modelling activities. For a more in-depth treatment of R, refer to this book’s Appendices and, for instance, (Wickham & Grolemund 2017, Peng 2019, Venables et al. 2021).
These pages contain solid underpinnings for further studies related to statistical learning, machine learning data science, data analytics, and artificial intelligence, including (Bishop 2006, Hastie et al. 2017, James et al. 2017). We will also appreciate the vital role of mathematics as a universal language for formalising data-intense problems and communicating their solutions. The book is aimed at readers who are yet to be fluent with university-level linear algebra, calculus and probability theory, such as 1st year undergrads or those who have forgotten all the maths they have learned and need a gentle, non-invasive, yet rigorous introduction to the topic. For a nice, machine learning-focused introduction to mathematics alone, see, e.g., (Deisenroth et al. 2020).
This book has been prepared with pandoc, Markdown, and GitBook. R code chunks have been processed with knitr. A little help of bookdown, good ol’ Makefiles, and shell scripts did the trick.
The following R packages are used or referred to in the text: bookdown, Cairo, DEoptim, fastcluster, FNN, genie, genieclust, gsl, hydroPSO, ISLR, keras, knitr, Matrix, microbenchmark, pdist, RColorBrewer, recommenderlab, rpart, rpart.plot, rworldmap, scatterplot3d, stringi, tensorflow, tidyr, titanic, vioplot.