797ML Handbook
2022-04-17
Chapter 1 About
This book is being written as part of a final project for 797ML at UMass Amherst, spring 2022. It contains a simple reference and breakdown for a couple of dozen core methods used in machine learning.
The intent is twofold:
- Serve as a reference for the basics of the material covered in the class, using language and examples that are as simple as possible to explain the core concepts and how to do them;
- Force myself to learn these techniques better by carrying out the above.
The main purpose of this work is to be simple, not to be comprehensive. We won’t cover every facet of every technique, or every possible permutations of outcomes. The goal is to simply express the broad strokes and core concepts in a way that can be easily remembered, and to serve as a jumping-off point when more information is needed.
1.2 Contact
Steve Linberg: steve@slinberg.net || https://slinberg.net
Project home: https://stevelinberg.github.io/797MS-handbook/
Github repo: https://github.com/stevelinberg/797MS-handbook
1.3 Resources
A lot of the material from this work is from the class textbook, James et al. (2021). I also find UNC geneticist Josh Starmer’s StatQuest video series on YouTube to be immensely helpful for simple explanations of statistics and machine learning concepts. His website statquest.org has many additional useful resources.