CSE 158/258: Web Mining and Recommender Systems

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

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

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 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.

Office hours:
TBD
Assessment:
Grading:

last year's course webpage
intro and course outline slides
1

Supervised Learning: Regression

Monday October 5 / Wednesday October 7:
Other resources:
Coursera slides (introductory):
Code examples:

Filesweek1.py50k beer reviewsnon-alcoholic beer reviews
Lecture materials slides + annotations
2

Supervised Learning: Classification

Monday October 12 / Wednesday October 14:
Other resources:
Coursera slides:
Code examples:

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

Dimensionality Reduction and Clustering

Monday October 19 / Wednesday October 21:
Other resources:
Code examples:

Filesweek3.pyfacebook ego network
Lecture materials slides + annotations
4

Recommender Systems

Monday October 26 / Wednesday October 28:
Other resources:
Coursera slides:
Code examples:

Filesweek4.py
Lecture materials slides + annotations
5

Text Mining

Monday November 2 / Wednesday November 4:
Other resources:
Code examples:

Filesweek5.py
Lecture materials slides + annotations
6

(Take-home) Midterm due

Midterm dueNov 9
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)
6

Tools and Libraries

No lecture November 11 (Veteran's Day)
Monday November 9:
Code examples:

Filesweek6.py
Lecture materials slides + annotations
7

Data Mining in Social Networks

Monday November 16 / Wednesday November 18
Other resources:

Lecture materials slides + annotations
8

State-of-the-art Recommender Systems

No lecture November 27 (Thanksgiving)
Monday November 25

Lecture materials slides + annotations
9

Online Advertising

Monday November 30:
Wednesday December 2:

Lecture materials slides + annotations
10

Modeling Temporal and Sequence Data

Monday December 7 / Wednesday December 9
Code examples:

Filesweek10.py
Lecture materials slides + annotations