"All that befalls you is part of the great Web." | |
-Marcus Aurelius |
CSE 158 is an undergraduate 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 2. Meetings are in Galbraith Hall.
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 additional office hours from 10:00-13:00, Mondays and Fridays in CSE B275. 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). You are also allowed a single "late day" for any report, i.e., if you submit one report late by one day there will be no penalty, but further late reports will not be graded.
Week | Topics | Files | References | Slides | Podcast | Homework |
---|---|---|---|---|---|---|
1 (Oct 2/4) | 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 Oct 16 |
2 (Oct 9/11) | 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) case study: reddit popularity |
lecture 3 lecture 4 |
|
3 (Oct 16/18) | 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) case study: social circes |
lecture 5 lecture 6 |
Homework 2 due Oct 30 |
Week | Topics | Files | References | Slides | Podcast | Homework |
---|---|---|---|---|---|---|
4 (Oct 23/25) | Recommender Systems
|
Elkan ch.11 |
lecture 7 (w/ annotations) lecture 8 (w/ annotations) assignment 1 |
lecture 7 lecture 8 |
Assignment 1 due Nov 20 |
|
5 (Oct 30/Nov 1) | 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 13 |
6 (Nov 6/8) | MIDTERM (Nov 8)
|
sp15 midterm (CSE190) fa15 midterm (CSE190) fa15 midterm (CSE255) wi17 midterm (CSE158) wi17 midterm (CSE258) |
midterm review (w/ annotations) |
midterm prep |
Assignment 2 due Dec 4 |
|
7 (Nov 13/15) | Network Analysis
|
Elkan ch.14 Easley & Kleinberg |
lecture 11 (w/ annotations) lecture 12 (w/ annotations) |
lecture 11 lecture 12 |
Homework 4 due Nov 27 |
|
8 (Nov 20/22) | Online advertising
|
tensorflow.py |
Mining Massive Datasets |
lecture 13 (w/ annotations) lecture 14 (w/ annotations) |
lecture 13 lecture 14 |
|
9 (Nov 27/29) | State-of-the-art Recommender Systems
|
lecture 15 and 16 (w/ annotations) |
lecture 15 lecture 16 |
|||
10 (Dec 4/6) | Modeling Temporal and Sequence Data
|
week10.py |
lecture 17 (w/ annotations) lecture 18 (w/ annotations) |
lecture 17 lecture 18 |