Data Mining 290

Description

Learn how to obtain, clean, visualize, understand, model, and predict the world around you using data. Grading will consist of homework (30%), midterm (30%), project (40%).

Instructor

Jim Blomo <jblomo@ischool>

GSI

Shreyas <shreyas@ischool>

Textbook

Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques, Third Edition (3rd ed.). Morgan Kaufmann.

Syllabus

DM[0-9]+ indicates chapters from the text, Data Mining.

Date	Readings	Slides	Homework / Project
Jan 25	Try Github ; A Taxonomy of Data Science	Class Intro ; Tools Intro by GUEST: Shreyas	Git Intro
Feb 1	DM1 ; The Yelp Factor: Are Consumer Reviews Good for Business?	Case Studies ; Obtaining Data	Obtain & Explore Data
Feb 8	DM2, DM3	Probability ; Preprocessing	Data Stats
Feb 15	DM4, Apache Hadoop: Petabytes and Terawatts (slides); mrjob docs (for homework)	Data Warehouse ; MapReduce	Project Details ; mrjob
Feb 22	DM8	Decision Trees; Naive Bayes	Gini Index
Mar 1	DM[9.1-9.3], 9.5 ; Understanding the Bias-Variance Tradeoff	SVM ; Neural Networks	Neural Network Back Propagation
Mar 8	DM10	Agglomerative - Clustering ; Hierarchical, Density - Clustering	K-Means
Mar 15	DM11.1	Review	prepare 1 cheat sheet
Mar 22	1 cheat sheet	Midterm	-
Mar 29	HOLIDAY
Apr 5	DM6	Advanced Clustering ; Frequent Pattern	AWS ; Project Proposal Due
Apr 12	DM11.3; PageRank; Uncovering Social Network Sybils in the Wild	Graphs; PageRank	Adjacency Representations
Apr 19	Non-linear regression	GUEST: Gene Lee Ceaser’s Pricing Strategy; Ceaser’s Recruiting	Price Elasticity
Apr 26	DM12; Shazam Audio Search	Outliers; Images & Audio	Midterm Review
May 3	TBA	Visualization ; HTML	TBA
May 10	TBA	In Real Life ; Review	Project Papers Due
May 17	-	Final Presentation	Bye!