CSE 158/258 (MGTA461/DSC256): Web Mining and Recommender Systems

Instructor: Julian McAuley (jmcauley@eng.ucsd.edu)

Autumn 2023, Tuesday/Thursday 11:00-12:20, WLH (Warren Lecture Hall) 2001 and Twitch

Lectures will be broadcast to twitch and recent videos will be available at https://www.twitch.tv/julianmcauley/videos/all

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


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 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.


The textbook for this course is Personalized Machine Learning. A (draft) pdf of the textbook is available for download.

Additional references are 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 (though more introductory) material.

Office hours:

Office hours (and instructions to access) for each class are posted to Piazza


piazza page (CSE258, MGTA461)
piazza page (CSE158)
piazza page (DSC256)
gradescope page (CSE258, MGTA461); see Piazza for entry code
gradescope page (CSE158); see Piazza for entry code
gradescope page (DSC256); see Piazza for entry code
last year's course webpage
intro and course outline slides

Wk 0/1

Supervised Learning: Regression

Coursera slides (introductory):
Additional code examples:

Files50k beer reviewsnon-alcoholic beer reviews
Lecture materials lecture 1 video lecture 2 video lecture 3 video slides + annotations

Wk 1/2

Supervised Learning: Classification

Coursera slides:
Code examples:

Files50k book descriptions5k book cover images
Lecture materials lecture 4 video lecture 5 video slides + annotations

Wk 3/4/5

Recommender Systems

Coursera slides:
Code examples:

Lecture materials lecture 6 video lecture 7 video lecture 8 video lecture 9 video lecture 10 video slides + annotations assignment 1 slides

Wk 6


sp15 midterm (CSE190)SolutionsSolution video (starts at 49:55)
fa15 midterm (CSE190)SolutionsSolution video (starts at 35:10)
fa15 midterm (CSE255)SolutionsSolution video (starts at 32:25)
wi17 midterm (CSE158)SolutionsSolution video (starts at 42:00)
wi17 midterm (CSE258)SolutionsSolution video (starts at 46:00)
fa17 midterm (CSE158)SolutionsSolution video (starts at 35:50)
fa17 midterm (CSE258)SolutionsSolution video (starts at 40:15)
fa18 midterm (CSE158)SolutionsSolution video (starts at 55:50)
fa18 midterm (CSE258)SolutionsSolution video (starts at 45:00)
fa19 midterm (CSE158)SolutionsSolution video (starts at 01:45)
fa19 midterm (CSE258)SolutionsSolution video (starts at 01:45)
fa20 midterm (CSE258)SolutionsSolution video (starts at 34:45)
fa21 midterm (CSE258)SolutionsSolution video (starts at 05:00)
fa22 midtermSolutionsSolution video (starts at 50:40)

Lecture materials lecture 11 video (midterm prep)

Wk 6/7

Text Mining

Code examples:

Lecture materials lecture 12 video lecture 13 video slides + annotations assignment 2 slides

Wk 7/8

Content and Structure in Recommender Systems

Lecture materials lecture 14 video slides + annotations


Visual Recommendation

Lecture materials lecture 15 video slides + annotations


Modeling Temporal and Sequence Data

Lecture materials lecture 16 video lecture 17 video slides + annotations


Ethics and Fairness

Lecture materials lecture 18 video lecture 19 video slides + annotations