CSE 158/258: Web Mining and Recommender Systems
Autumn 2020, Monday/Wednesday 17:00-18:20 PST, Twitch
For those unable to access twitch, or attend the lecture time, all recordings will be posted here on the day following the lecture
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.
The course meets twice a week on Monday/Wednesday evenings, starting October 5. Meetings are livestreamed on twitch, but recordings will also be made available here.
There is no textbook for the course, though chapter references will be provided from Pattern Recognition and Machine Learning (Bishop), and from Charles Elkan's 2013 course notes. Links are also provided to our Coursera Specialization, which covers similar material.
- 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 26%.
- Each Assignment is worth 25%.
- Assignment 2 is a group assignment. All other assessment must be completed individually.
- All assessments are due before the Monday lecture on the due date. Late submissions are not accepted.
Supervised Learning: Regression
Monday October 5 / Wednesday October 7:
- Least-squares regression
- Overfitting and regularization
- Training, validation, and testing
Coursera slides (introductory):