This is not a math/stats class. In class we will briefly summarize how these methods work and spend the bulk of our time on estimating and interpreting these models. That said, you should have some understanding of the mathematical underpinnings of statistical learning methods prior to implementing them yourselves. See below for some recommended readings:
For those with little/no statistics training
Chapters 7-8 of OpenIntro Statistics - an open-source statistics textbook written at the level of an introductory undergraduate course on statistics
Chapters 4-5 in Hands-On Machine Learning with R - a recent publication which approaches these methods from the perspective of machine learning rather than traditional statistical inference. Includes code examples using R and the caret package.
caret - a package which unifies hundreds of separate algorithms for generating statistical/machine learning models into a single standardized interface. Very robust, but pre-tidyverse and on the path to deprecation.
tidymodels - a collection of packages for machine and statistical learning using tidyverse principles.