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CSE 190: Dahta Mining and Predictive Analytics

Spring 2015, Tuesday/Thursday 18:30-19:50, CENTR 113

For the current version of this class, see here

CSE 190 is an undergraduate 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 twice a week on Tuesday/Thursday evenings, starting March 31. Meetings are in CENTR 113.

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 Wednesday 1-3pm in CSE 4102. Additional office hourse will be held by Long Jin (Friday 12:30-2:30pm in EBU3B B275) and Pranay Kumar Myana (Monday 5-7pm in EBU3B B250A). For other discussions see the course's Piazza page.

Part 1: Methods

Week	Topics	Files	References	Slides	Podcast	Homework
1 (Mar 31/Apr 2)	Supervised Learning: Regression Least-squares regression Overfitting & regularization Training, validation, and testing	50k beer reviews non-alcoholic beer reviews week1.py	Bishop ch.3 Elkan ch.3,6	lecture 1 lecture 2 case study: reddit	lecture 1 lecture 2	Homework 1 due April 14
2 (Apr 7/9)	Supervised Learning: Classification Logistic regression SVMs Multiclass & multilabel classification How to evaluate classifiers	50k book descriptions 5k book cover images week2.py	Bishop ch.4 Elkan ch.5,8	lecture 3 lecture 4	lecture 3 lecture 4
3 (Apr 14/16)	Dimensionality Reduction & Clustering Singular value decomposition & PCA K-means & hierarchical clustering Community detection	facebook ego network week3.py	Bishop ch.9 Elkan ch.13	lecture 5 lecture 6 case study: social circes	lecture 5 lecture 6	Homework 2 due April 28
4 (Apr 2)	Graphical Models & Interdependent Variables Directed and undirected models		Bishop ch.8	lecture 7	lecture 7

Part 2: Applications

Week	Topics	Files	References	Slides	Podcast	Homework
4/5 (Apr 23/28/30)	Recommender Systems Latent-factor models Collaborative filtering	assignment 1 data homework 3 data baselines.py	Elkan ch.11	lecture 8 assignment 1 lecture 9 case study: beer experts lecture 10 (midterm review)	lecture 8 lecture 9 lecture 10	Homework 3 due May 12 Assignment 1 due May 20
6 (May 5/7)	MIDTERM (May 5) and Text Mining (part 1)	week6.py	Elkan ch.12	lecture 11 assignment 2	lecture 11	Assignment 2 due June 2 reports
7 (May 12/14)	Guest lecture (Manuel Gomez Rodriguez, May 12) Miscellaneous stuff (May 14) Assignment 1 hints and tips Assignment 2 Midterm recap	homework 3.2 solution		guest lecture lecture 12	guest lecture lecture 12	Homework 4 due May 26
8 (May 19/21)	Text Mining (part 2) Sentiment analysis Bags-of-words TFIDF Stopwords, stemming, and topic models		Elkan ch.12	lecture 13 lecture 14	lecture 13 lecture 14
9 (May 26/28)	Network Analysis Power-laws and small-worlds Random graph models triads and weak ties HITS and PageRank		Elkan ch.14 Easley & Kleinberg	lecture 15 lecture 16	lecture 15 lecture 16
10 (Jun 2/4)	Modeling Temporal and Sequence Data Sliding windows and autoregression Hidden Markov Models Temporal dynamics in recommender systems Temporal dynamics in text and social networks	week10.py		lecture 17 lecture 18	lecture 17 lecture 18