CSE 158/258: Web Mining and Recommender Systems

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

Autumn 2021, Monday/Wednesday 17:00-18:20 PST, GA AUD (Torrey Pines) 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

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 Monday/Wednesday evenings, starting September 27. The class meets in GA AUD (Torrey Pines), though meetings will also be livestreamed on twitch. Recordings will also be made available on this page after the class.

Textbook:

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

Assessment:
Grading:

piazza page (CSE258)
piazza page (CSE158)
last year's course webpage
intro and course outline slides

1

Supervised Learning: Regression

Monday September 27 / Wednesday September 29:
References:
Coursera slides (introductory):
Additional code examples:

Filesweek1.py50k beer reviewsnon-alcoholic beer reviewsgoodreads fantasy reviews
Lecture materials lecture 1 video lecture 2 video slides + annotations

2

Supervised Learning: Classification

Monday October 4 / Wednesday October 6:
References:
Coursera slides:
Code examples:

Filesweek2.py50k book descriptions5k book cover images
Lecture materials lecture 3 video lecture 4 video slides + annotations

3/4

Recommender Systems

Monday October 11 / Wednesday October 13 / Monday October 18 / Wednesday October 20
References:
Kaggle pages (Assignment 1):
Coursera slides:
Code examples:

Filesassignment 1 data
Lecture materials lecture 5 video lecture 6 video lecture 7 video lecture 8 video slides + annotations assignment 1 slides

5

Text Mining

Monday October 25 / Wednesday October 27
References:
Code examples:

6

Midterm

MidtermNov 3
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)

7

Content and Structure in Recommender Systems

Monday November 8 / Wednesday November 10:
References:

8

Modeling Temporal and Sequence Data

Monday November 15 / Wednesday November 17
References:

9

Visual Recommendation

No lecture November 24 (Thanksgiving); maybe something fun?!?
Monday November 22:
References:

10

Ethics and Fairness

Monday November 29 / Wednesday December 1:
References: