"All that befalls you is part of the great Web." | |
-Marcus Aurelius |
CSE 258 is a graduate course 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 January 9. Meetings are in Peterson Hall 108.
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.
Office hours: I'll hold office hours on Tuesdays 9:00-13:00 in CSE 4102. The course TAs will hold office hours on Mondays and Fridays 10:00-13:00pm in B250A. For other discussions see the course's Piazza page.
Note that there will be no class on Jan 16 (MLK Day) or Feb 20 (Presidents' Day).
Week | Topics | Files | References | Slides | Podcast | Homework |
---|---|---|---|---|---|---|
1 (Jan 9/Jan 11) | Supervised Learning: Regression
|
50k beer reviews non-alcoholic beer reviews week1.py |
Bishop ch.3 Elkan ch.3,6 |
introduction & outline lecture 1 (w/ annotations) lecture 2 (w/ annotations) |
lecture 1 lecture 2 |
Homework 1 due Jan 23 |
2/3 (Jan 18/23) | Supervised Learning: Classification
|
50k book descriptions 5k book cover images week2.py |
Bishop ch.4 Elkan ch.5,8 |
lecture 3 (w/ annotations) lecture 4 (w/ annotations) |
lecture 3 lecture 4 |
Homework 2 due Feb 6 |
3/4 (Jan 25/30) | Dimensionality Reduction & Clustering
|
facebook ego network week3.py assignment 1 data |
Bishop ch.9 Elkan ch.13 |
lecture 5 (w/ annotations) lecture 6 (w/ annotations) |
lecture 5 lecture 6 |
Assignment 1 due Feb 27 |
Week | Topics | Files | References | Slides | Podcast | Homework |
---|---|---|---|---|---|---|
4/5 (Feb 1/6) | Recommender Systems
|
Elkan ch.11 |
lecture 7 (w/ annotations) lecture 8 (w/ annotations) assignment 1 |
lecture 7 lecture 8 |
Homework 3 due Feb 20 |
|
5/6 (Feb 8/13) | Text Mining
|
week5.py |
Elkan ch.12 |
lecture 9 (w/ annotations) lecture 10 (midterm review) (w/ annotations) |
lecture 9 lecture 10 |
|
6 (Feb 15) | MIDTERM |
fa15 midterm (CSE255) fa15 midterm (CSE190) sp15 midterm (CSE190) week6.py |
Assignment 2 due Mar 13 |
|||
7 (Feb 22) | Text Mining ctd. |
lecture 11 (w/ annotations) assignment 2 |
lecture 11 |
Homework 4 due Mar 6 |
||
8 (Feb 27/Mar 1) | Network Analysis
|
Elkan ch.14 Easley & Kleinberg |
lecture 12 (w/ annotations) lecture 13 (w/ annotations) |
lecture 12 lecture 13 |
||
9 (Mar 6/8) | Online advertising
|
tensorflow.py |
Mining Massive Datasets |
lecture 14 (w/ annotations) lecture 15 (w/ annotations) |
lecture 14 lecture 15 |
|
10 (Mar 13/15) | Modeling Temporal and Sequence Data
|
week10.py |
lecture 16 (w/ annotations) lecture 17 (w/ annotations) |
lecture 16 lecture 17 |