There is no difference between the in-person and remote versions of the course. The remote version will be live-streamed from the classroom
If you are enrolled in an in-person section (158/258) you are welcome to attend remotely
If you are enrolled in a remote section, you should attend remotely at least for the first few weeks. You are welcome to attend some lectures if there's room later in the quarter
For the most part, you should enrol in whichever section has room
158 vs 258
The graduate (258) and undergraduate (158) versions of the course have largely overlapping (60-70%) content
While the content in 258 is sometimes a little more difficult, the difference is not big
It is hard to say which class is "easier" in terms of grading since the two classes have somewhat different student populations and grading standards
Undergraduates are welcome to enrol in 258 if they would like, though usually only a dozen or so students do so each year
General content questions
No version of the class has a final exam (one may appear on the schedule for some sections but it is just a placeholder)
The class has no synchronous components (the midterm is a takehome with a window of at least several hours)
All content will be recorded and available throughout the quarter
If you want to get ahead on the material, go through last year's version (below), or read the course textbook
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 28. The class meets in WLH (Warren Lecture Hall) 2001, though meetings will also be livestreamed on twitch. Recordings will also be made available on this page after each class.
