CSE158 / CSE258 / MGTA461 / DSC256: Web Mining and Recommender Systems


Instructor	Julian McAuley
Room	Warren Lecture Hall (WLH) 2001
Days & times	11:00am-12:20pm, Tuesdays & Thursdays
Office hours	posted on Piazza

[piazza] [gradescope] [twitch] [podcast]

Description

CSE 158 and 258 (also offered as MGTA461 and DSC256) 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 25. The class meets in Warren Lecture Hall 2001, though meetings will also be livestreamed on twitch. Recordings will also be made available on this page after each class.

Textbook:

The textbook for this course is Personalized Machine Learning (PML). 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 section will be posted to Piazza.

Schedule

week	slides	textbook ref
	course introduction
1-2	supervised learning: regression	ch. 2 + code
2-3	supervised learning: classification	ch. 3
4-6	recommender systems	ch. 4, ch. 5
7-8	text mining	ch. 8
9	content and structure in recommender systems	ch. 6
10	modeling temporal and sequence data	ch. 7

[slides] [notebooks from lectures] [datasets]

Assignments

All work must be completed on your own unless otherwise specified.
Please submit your work to gradescope. See piazza for entry code.
Each Homework is worth 8 marks.
Note: One homework will be dropped (or you can skip one), so that the total homework grade is 24 points
Assignment 1 is worth 27 marks.
Assignment 2 is worth 25 marks.
Peer grading for Assignment 2 is worth 4 marks.
Assignment 2 is a group assignment. All other assessment must be completed individually.
Late submissions are not accepted. You're welcome to submit during the late submission window on gradescope (usually a few hours) without penalty but this is only there so that nobody can make the excuse that they missed the submission time by a few minutes. Some assessments, such as the first assignment have hard deadlines.

Homework	24 marks	Assignments	56 marks
├ Homework 1	8 marks	├ Assignment 1	27 marks
├ Homework 2	8 marks	├ Assignment 2	25 marks
├ Homework 3	8 marks	├ peer grading	4 marks
├ Homework 4	8 marks

The above items add up to 80 marks. I tend to follow a "normal" grading scale (As in the 90s, Bs in the 80s, etc.) after multiplying your mark by 100/80.