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%), a midterm (30%), and a project (40%).

Instructor

Jimmy Retzlaff <jretz@ischool>

GSI

Shreyas <shreyas@ischool>

Textbook

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

Course Discussion

Info 290T: Data Mining on Piazza

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Syllabus

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

Date	Readings	Slides	Homework / Project
Jan 23	Try Github ; A Taxonomy of Data Science	Class Intro ; Tools Intro by GUEST: Shreyas	Git Intro
Jan 30	DM1 ; The Yelp Factor: Are Consumer Reviews Good for Business?	Case Studies ; Obtaining Data	Obtain & Explore Data
Feb 6	DM2, DM3	Probability ; Preprocessing	Data Stats
Feb 13	DM4, Apache Hadoop: Petabytes and Terawatts (slides); mrjob docs (for homework)	Data Warehouse ; MapReduce	Project Details ; mrjob
Feb 20	DM8	Decision Trees; Naive Bayes	Gini Index
Feb 27	DM[9.1-9.3], 9.5 ; Understanding the Bias-Variance Tradeoff	SVM ; Neural Networks	Neural Network Back Propagation
Mar 6	DM10	Clustering - Partitioning ; Clustering - Hierarchical & Density	K-Means
Mar 13	DM11.1	Review	prepare 1 cheat sheet
Mar 20	1 cheat sheet	Midterm
Mar 27	HOLIDAY
Apr 3	DM6	Advanced Clustering ; Frequent Patterns	AWS ; Project Proposal due April 9
Apr 10	DM11.3; PageRank; Uncovering Social Network Sybils in the Wild	Graphs; PageRank	Adjacency Representations
Apr 17	DM12; Shazam Audio Search	Outliers; Images & Audio	Midterm Review
Apr 24	Embedded Plots ; Data-Driven Documents	Visualization ; Yelp's Visualizations	D3 Intro; D3 Lab
May 1	A Few Useful Things to Know about Machine Learning ; Top 10 Algorithms in Data Mining	In Real Life	Project Data and Presentation due May 8th
May 8		Final Presentation	Project Code & Papers due May 14th
May 15			Bye!