| "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 October 1. 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 9:30-13:00 in CSE 4102. The course TAs will hold additional office hours as follows:
| Day | Time | Location |
|---|---|---|
| Monday | 8:30am - 10:00am | B240A |
| Monday | 1:00pm - 2:30pm | B250A |
| Friday | 1:30pm - 3:30pm | B215 |
| Friday | 2:00pm - 4:00pm | Rady 4N128 |
For other discussions see the course's Piazza page.
Grading: All reports will be submitted via gradescope, and (except where the spec states otherwise) are expected to be completed individually. Your lowest (of four) homework scores will be discarded (or you are welcome not to submit one of the homeworks).
| Week | Topics | Files | References | Slides | Podcast | Homework |
|---|---|---|---|---|---|---|
| 1 (Oct 1/3) | 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 (from FA17) lecture 2 |
Homework 1 due Oct 15 |
| 2 (Oct 8/10) | 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 |
|
| 3 (Oct 15/17) | Dimensionality Reduction & Clustering
|
facebook ego network week3.py |
Bishop ch.9 Elkan ch.13 |
lecture 5 (w/ annotations) lecture 6 (w/ annotations) case study: reddit |
lecture 5 lecture 6 |
Homework 2 due Oct 29 |
| Week | Topics | Files | References | Slides | Podcast | Homework |
|---|---|---|---|---|---|---|
| 4 (Oct 22/24) | Recommender Systems
|
Elkan ch.11 |
lecture 7 (w/ annotations) lecture 8 (w/ annotations) assignment 1 |
lecture 7 lecture 8 |
Assignment 1 due Nov 19 |
|
| 5 (Oct 29/Oct 31) | Text Mining
|
week5.py |
Elkan ch.12 |
lecture 9 (w/ annotations) lecture 10 (w/ annotations) assignment 2 |
lecture 9 lecture 10 |
Homework 3 due Nov 14 |
| 6 (Nov 5/7) | MIDTERM
|
sp15 midterm (CSE190) fa15 midterm (CSE190) fa15 midterm (CSE255) wi17 midterm (CSE158) wi17 midterm (CSE258) fa17 midterm (CSE158) fa17 midterm (CSE258) |
midterm review (w/ annotations) |
midterm prep |
Assignment 2 due Dec 3 |
|
| 7 (Nov 14) | Tools and Libraries
|
tools.py | slides | podcast |
Homework 4 due Nov 26 |
|
| 8/9 (Nov 19/26) | Network Analysis
|
Elkan ch.14 Easley & Kleinberg |
lecture 11 (w/ annotations) lecture 12 (w/ annotations) |
lecture 11 lecture 12 |
||
| 9/10 (Nov 28/Dec 3) | Online advertising
|
Mining Massive Datasets |
lecture 13 and 14 (w/ annotations) |
lecture 13 lecture 14 |
||
| 10 (Dec 3/5) | Modeling Temporal and Sequence Data
|
week10.py |
lecture 15 (w/ annotations - FA17) lecture 16 (w/ annotations) |
lecture 15 lecture 16 |