ECS 172 Syllabus, Winter 2022

(Subject to change, due to Covid-19.)

Instructors:

Contents (somewhat long, but VITAL):

Goals:

Prerequisites:

Textbook:

Instructor's notes. Will be available here.

Class format:

Lecture periods, MW, 9:00-9:50, Hoagland 168, F 10-11 Wellman 212:

Discussion sections, M 10-10:50, Kerr 212:

NOTE: For now, and likely throughout the quarter, the TA will swap time slots with me on Fridays. I will lecture in the 10 a.m. slot, and the TA wlll conduct the discussion section at 9 a.m.

Groups:

You will rely quite heavily on your homework groups for homework, the term project and the group quiz. Get to know them well!

Quizzes:

Homework:

Term project:

Blog:

It is required that you read the course blog at least once per day. All course announcements will be made there. Material in blog posts may also be subject to exam questions.

Grading:

Homework/project submission details:

It's vital to get these steps 100% correct. Remember, a script will be preprocessing your submission.

Topical coverage:

Prologue: Overview of the Recommender Systems Field

Collaborative filtering; content-based methods; the "Hello, World" dataset, MovieLens.

Part I: Infrastructure

Part II: Collaborative Filtering

Part III: Content-Based Methods

Part IV: Misc.

Math R for Quizzes:

Case Study of Bonuses in Course Grade:

This was a student in ECS 132.