CSE 255 is a graduate-level course devoted to current methods for 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 once a week on Monday evening, starting January 5. There will be no classes on January 19 (MLK day) or on February 16 (President's day). Meetings are in CENTR 222.
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 Friday 9-11am in CSE 4102. Additional office hours will be held by Dongcai Shen on Mondays 10-12 in CSE 4127. For other discussions see the course's Piazza page.
Week | Topics | Files | References | Slides | Homework |
---|---|---|---|---|---|
1 (Jan 5) | Supervised Learning: Regression
|
50k beer reviews lecture1.py |
Bishop ch.3 Elkan ch.3,6 |
introduction course outline lecture 1 case study: reddit |
Homework 1 due January 12 |
2 (Jan 12) | Supervised Learning: Classification
|
50k book descriptions 5k book cover images lecture2.py homework2.py |
Bishop ch.4 Elkan ch.5,8 |
lecture 2 |
Homework 2 Homework 3 both due January 26 |
3 (Jan 26) | Dimensionality Reduction & Clustering
|
facebook ego network lecture3.py |
Bishop ch.9 Elkan ch.13 |
lecture 3 assignment 1 case study: social circes |
Homework 4 due February 2 Assignment 1 due February 23 reports |
4 (Feb 2) | Graphical Models & Interdependent Variables
|
Bishop ch.8 |
lecture 4 case study: image labeling |
Homework 5 due February 9 |
Week | Topics | Files | References | Slides | Homework |
---|---|---|---|---|---|
5 (Feb 9) | Recommender Systems
|
homework 6/7 data assignment 2 data baselines.py |
Elkan ch.11 |
lecture 5 assignment 2 case study: beer experts |
Homework 6 Homework 7 both due February 23 (or morning of February 25 outside 4102) Assignment 2 due March 10 |
6 (Feb 23) | Text Mining
|
lecture6.py |
Elkan ch.12 |
lecture 6 case study: text and opinions |
Homework 8 due March 2 |
7 (Mar 2) | Network Analysis
|
Elkan ch.14 Easley & Kleinberg |
lecture 7 case study: rich-clubs |
Homework 9 due March 9 |
|
8 (Mar 9) | Modeling Temporal and Sequence Data
|
lecture8.py |
lecture 8 |
no homework! |