CSE/STAT 416 Book

Author: Hunter Schafer

This book is designed for the CSE/STAT 416 course at the University of Washington. The course is designed to teach machine learning to a wide audience. We assume comparatively less mathematical background than other ML books to make the content more accessible to more students. Our mission is to enable anyone who is interested to learn machine learning which means our job is to make tough concepts intuitive and applicable. Read more in the introduction.

This books is very much a work in progress. We are always interested in hearing your feedback so please let us know what you think with one of the feedback avenues below.

Table of Contents

  1. Introduction

  2. Regression: Housing Prices

    1. Linear Regression
    2. Assessing Performance
    3. Regularization: Ridge

  3. Classification: Review Sentiment

    1. Coming Soon

  4. Clustering and Similarity: Similar Articles

    1. Coming Soon

  5. Recommender Systems: Product Recommendation

    1. Coming Soon

  6. Deep Learning: Image Recognition

    1. Coming Soon
  7. Tufte (layout example)

Acknowledgements

CSE/STAT 416 was originally developed at UW by Emily Fox based on the Coursera course created by her and Carlos Guestrin. Hunter was a major contributor to the adaption of the online class to its first offering at UW, and has recently been the caretaker of the course as it continues to evolve. While the format, text, and animations of the book are Hunter's creations, this book wouldn't exist without Emily and Carlos' hard work in building a story for teaching ML to a broader audience.

All of the code for this book (including the animations) can be found on our GitHub repository.