Data Mining
Instructor : Jeff Phillips (email) | Office hours: Thursday morning 11am-12noon @ MEB 3442 (and directly after class in WEB L104)
TAs: WaiMing Tai (email) | Office hours: 11am-noon Friday, MEB 3115
      + Deb Paul (email) | Office Hours: 3-4pm Thursday, MEB 3115
      + Yang Gao (email) | Office Hours: 9-11am Tuesdays, MEB 3115
      + Shweta Singhal (email) | Office Hours: 12:10-2:10pm Tuesdays, MEB 3115
      + Yogesh Mishra (email) | Office Hours: 5pm-7pm Mondays, MEB 3115
Spring 2017 | Mondays, Wednesdays 3:00 pm - 4:20 pm
WEB L104
Catalog number: CS 5140 01 or CS 6140 01

Syllabus
Description:
Data mining is the study of efficiently finding structures and patterns in large data sets. We will focus on several aspects of this: (1) converting from a messy and noisy raw data set to a structured and abstract one, (2) applying scalable and probabilistic algorithms to these well-structured abstract data sets, and (3) formally modeling and understanding the error and other consequences of parts (1) and (2), including choice of data representation and trade-offs between accuracy and scalability. These steps are essential for training as a data scientist.
Algorithms, probability, and linear algebra are required mathematical tools for understanding these approaches.
Topics will include: similarity search, clustering, regression/dimensionality reduction, graph analysis, PageRank, and small space summaries. We will also cover several recent developments, and the application of these topics to modern applications, often relating to large internet-based companies.
Upon completion, students should be able to read, understand, and implement ideas from many data mining research papers.

Books:
We will in general not follow any book. My own course notes (linked below) serve as the defacto book. However, the following two free online books may serve as useful references that have good overlap with the course.
MMDS(v1.3): Mining Massive Data Sets by Anand Rajaraman, Jure Leskovec, and Jeff Ullman. The digital version of the book is free, but you may wish to purchase a hard copy.
FoDS: Foundations of Data Science by Avrim Blum, John Hopcroft and Ravindran Kannan. This provide some proofs and formalisms not explicitly covered in lecture.

Videos: We plan to videotape all lectures, and make them available online. They will appear on this playlist on our YouTube Channel.
Videos will also livestream here.
Lectures will also be live-streamed and available through Luum at this course link.

Prerequisits: A student who is comfortable with basic probability, basic linear algebra, basic big-O analysis, and basic programming and data structures should be qualified for the class. There is no specific language we will use. However, programming assignments will often (intentionally) not be as specific as in lower-level classes. This will partially simulate real-world settings where one is given a data set and asked to analyze it; in such settings even less direction is provided.
For undergrads, the prerequisits are CS 3500 and CS 3130 and MATH 2270 (or equivalent), and CS 4150 is a corequisite. I will grant exceptions for those with (a reasonable grade in) CS 4964 (Fall 2016).
In the past, this class has had undergraduates, masters, and PhD students, including many from outside of Computer Science. Most (but not all) have kept up fine, and still most have been challenged. If you are unsure if the class is right for you, contact the instructor.

For an example of what sort of material I expect to be familiar, see these notes on probability and linear algebra. The rest of the notes from this class may also be useful for review.
Schedule: (subject to change - some linked material is from the previous iteration of the class)
Date Topic (+ Notes) Video Link Assignment (latex) Project
Mon 1.09 Class Overview Vid MMDS 1.1
Wed 1.11 Statistics Principles (+ Chernoff Bounds) Vid MMDS 1.2
Mon 1.16 (MLK Day - No Class)
Wed 1.18 Similarity : Jaccard + k-Grams (S) Vid MMDS 3.1 + 3.2 | FoDS 7.3
Mon 1.23 Similarity : Min Hashing Vid MMDS 3.3
Wed 1.25 Similarity : LSH Vid MMDS 3.4 Statistical Principles
Mon 1.30 Similarity : Distances Vid MMDS 3.5 + 7.1 | FoDS 8.1 Proposal
Wed 2.01 Similarity : SIFT and ANN vs. LSH Vid MMDS 3.7 + 7.1.3
Mon 2.06 Clustering : Hierarchical Vid MMDS 7.2 | FoDS 8.7
Wed 2.08 Clustering : K-Means Vid MMDS 7.3 | FoDS 8.3
Mon 2.13 Clustering : Spectral (S) Vid MMDS 10.4 | FoDS 8.4 | Speilman | Gleich Document Hash
Wed 2.15 Streaming : Misra-Greis and Frugal Vid MMDS 4.1 | FoDS 7.1.3 | Min-Count Sketch | Misra-Gries
Mon 2.20 (Presidents Day - No Class)
Wed 2.22 Streaming : Count-Min + Apriori Algorithm Vid MMDS 6+4.3 | Careful Bloom Filter Analysis Data Collection Report
Mon 2.27 Regression : Basics in 2-dimensions Vid Jeff Notes | ESL 3.2 and 3.4
Wed 3.01 Regression : SVD + PCA Vid Jeff LA Review | Geometry of SVD - Chap 3 | FoDS 4 Clustering
Mon 3.06 Regression : Matrix Sketching Vid MMDS 9.4 | FoDS 2.7 + 7.2.2 | arXiv
Wed 3.08 MIDTERM TEST
Mon 3.13 (Spring Break - No Class)
Wed 3.15 (Spring Break - No Class)
Mon 3.20 Regression : Random Projections Vid FoDS 2.9 Intermediate Report
Wed 3.22 Regression : Compressed Sensing and OMP Vid FoDS 10.3 | Tropp + Gilbert Frequent
Mon 3.27 Regression : L1 Regression and Lasso Vid Davenport | ESL 3.8 | bias-variance example
Wed 3.29 Noise : Noise in Data Vid MMDS 9.1 | Tutorial
Mon 4.03 Lecture on Ethics/Fairness -- By Suresh Vid 10 Simple Rules for Responsible Big Data Research
Wed 4.05 Noise : Privacy Vid McSherry | Dwork
Mon 4.10 Graph Analysis : Markov Chains (S) Vid MMDS 10.1 + 5.1 | FoDS 5 | Weckesser notes
Wed 4.12 Graph Analysis : PageRank Vid MMDS 5.1 + 5.4 Regression
Mon 4.17 Graph Analysis : MapReduce Vid MMDS 2 | Final Report
Wed 4.19 Graph Analysis : Communities Vid MMDS 10.2 + 5.5 | FoDS 8.8 + 3.4 Poster Outline
Mon 4.24 ENDTERM TEST
Mon 5.01 Graphs
Tue 5.02 Poster Day !!! (3:30-5:30pm) Poster Presentation



Latex: I highly highly recommend using LaTex for writing up homeworks. It is something that everyone should know for research and writing scientific documents. This linked directory contains a sample .tex file, as well as what its .pdf compiled outcome looks like. It also has a figure .pdf to show how to include figures.