For those unable to access twitch, or attend the lecture time, all recordings will be posted below and to the UCSD Podcast page
Basic Info
Intro:
CSE 158 and 258 are undergraduate and graduate courses devoted to current methods for recommender systems, data mining, and predictive analytics. No previous background in machine learning is required, but all participants should be comfortable with programming (all example code will be in Python), and with basic optimization and linear algebra.
Lectures:
The course meets twice a week on Tuesday/Thursday mornings, starting September 26. The class meets in the Jeannie Auditorium, though meetings will also be livestreamed on twitch. Recordings will also be made available on this page after each class.
Each Homework is worth 8%. Your lowest (of four) homework grades is dropped (or one homework can be skipped).
The (take-home) Midterm is worth 25%.
Assignment 1 is worth 22%.
Assignment 2 is worth 25%.
Peer grading for Assignment 2 is worth 4%.
Assignment 2 is a group assignment. All other assessment must be completed individually.
Late submissions are not accepted. You're welcome to submit during the late submission window on gradescope (usually a few hours) without penalty but this is only there so that nobody can make the excuse that they missed the submission time by a few minutes. Some assessments, such as the first assignment and midterm, have hard deadlines.
