(pronouns: he · him)
Assistant Teaching Professor
Paul G. Allen School of Computer Science & Engineering
University of Washington
hschafer [at] cs [dot] washington [dot] edu
Paul G. Allen Center, Office 530
Interested in applying to be a TA with me?
Hunter Schafer is an assistant teaching professor in the Paul G. Allen School for Computer Science and Engineering at the University of Washington. Hunter received his B.S in Computer Science in 2016 and his M.S. in Computer Science in 2018, both from the University of Washington.
Hunter primarily teaches introduction to programming, data structures and algorithms, data science, and machine learning. He focuses on making these essential topics accessible to students from varying fields of study across campus. Hunter led the effort in creating the materials for CSE 163 and was a significant contributor to the development of CSE/STAT 416.
Courses are ordered from most-recently taught to least-recently taught. Quarters are in reverse-chronological order, with the most recent quarter for each course in bold.
|CSE 163||Winter 2022 - Winter 2021 - Spring 2020 - Winter 2020 - Spring 2019|
|CSE 143||Autumn 2021 - Autumn 2019 - Winter 2019 - Autumn 2018 - Summer 2017|
Winter 2022 - Autumn 2021 - Spring 2021 - Autumn 2020 - Spring 2020*
*Co-taught with Josh Ervin
|CSE 390HA||Autumn 2021 - Spring 2021 - Spring 2020 - Autumn 2019|
Spring 2021 - Summer 2019 - Spring 2018*
*Emily Fox lectured, Hunter helped develop course materials
|CSE 312||Winter 2021|
|CSE 373||Autumn 2020|
We are starting to offer CSE 163: Intermediate Data Programming at neighboring high schools through the UW in the High School program. As part of teaching this course outside of UW, Hunter has made a publicly-accessible version of the lesson content used for CSE 163. This website is the textbook used by students in CSE 163 at UW, which includes interactive code examples, videos, practice problems and quizzes to help students master the course content.
Status: The content of the book is all present, but it's fairly simple in terms of functionality. Further development will be done to make the book more interactive and other useful features will be included.
CSE/STAT 416: Introduction to Machine Learning is a fairly unique course in the audience it targets. It assumes relatively little mathematical background compared to other Machine Learning courses, so the way concepts are discussed needs to be approached differently for students in the course. The hope is this book will serve as a useful resource for students in CSE/STAT 416 and students who want a more accessible introduction to Machine Learning.
The book uses manim to make visualizations demonstrating machine learning concepts to readers as they read. The hope is to convey an intuitive understanding of the mathematics by using a mix of modalities (text, equations, images, and animations).
Status: This book is very early in its development and only has a few chapters done, there are also likely many changes that will happen to the structure and text of the book. Progress will be made in the coming months.