Spring 2017: CS6220 Data Mining Techniques, Section 01

General Information

Lecture time: Mondays & Wednesdays 2:50 – 4:30 PM	Place: Science Engineering Complex, Room 136 (Building #83 on this campus map)
Instructor: Tina Eliassi-Rad	Office hours: Mondays 5:00 – 6:00 PM in WVH 242 Also, available by appointment. Email eliassi [at] ccs [dot] neu [dot] edu to setup appointment; begin the subject line with [sp17 cs6220].
TA: Xuan Han	TA office hours: Wednesdays and Thursdays 6:00 – 7:00 PM in WVH 242 Also, available by appointment. Email han.xua [at] husky [dot] neu [dot] edu to setup appointment; begin the subject line with [sp17 cs6220].

Overview

This 4-credit graduate-level course covers data mining and unsupervised learning. Its prerequisites are:

o Graduate level CS 5800 Minimum Grade of C- or Graduate level CS 7800 Minimum Grade of C-.

o Calculus and linear algebra.

o An introductory course on statistics and probability.

o Algorithms and programming (MATLAB, Python, or R).

Textbooks

This course does not have a designated textbook. The readings are assigned in the syllabus (see below). Here are some textbooks (all optional) related to the course.

Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. ISBN 0387952845. (free online)
Anand Rajaraman, Jurij Leskovec, and Jeffrey Ullman. Mining of Massive Datasets. v2.1, Cambridge University Press. 2014. (free online)
Christopher Bishop. Pattern Recognition and Machine Learning. ISBN 0387310738.
Tom Mitchell. Machine Learning. ISBN 0070428077.
Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. ISBN 0262018020.
Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. ISBN 1107422221.
David J. Hand, Heikki Mannila, Padhraic Smyth. Principles of Data Mining. ISBN 026208290X.
Jiawei Han, Micheline Kamber, Jian Pei. Data Mining: Concepts and Techniques, Third Edition. ISBN 0123814790.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. ISBN 0321321367.
Charu C. Aggarwal, Data Mining, The Textbook, Springer 2015.

Resources

Grading

o Homework assignments (3*10%)

o Midterm exam (20%)

o Final exam (20%)

o Class project (30%)

o 2-page proposal & 5-minute in-class pitch (8%)

· Should include answers to the following questions:

§ What is the problem?

§ Why is it interesting and important?

§ Why is it hard? Why have previous approaches failed?

§ What are the key components of your approach?

§ What data sets and metrics will be used to validate the approach?

o In-class presentation (10%)

o Final report (12%) – maximum 6 pages

· For guidance on writing the final report, see slide 70 of Eamonn Keogh's KDD'09 Tutorial on How to do good research, get it published in SIGKDD and get it cited!

· Follow ACM formatting guidelines.

Schedule/Syllabus (Subject to Change)

Lec #	Date	Topic	Readings & Notes
1	M 1/9	Introduction and Overview	o Chapter 1 of http://www.mmds.org/#book o http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf o http://eliassi.org/ml-science-2015.pdf
2	W 1/11	Frequent Itemsets & Association Rules	o Chapter 6 of http://www.mmds.org/#book o Optional: Sections 6.1-6.6 of http://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf
–	M 1/16	MLK Jr Day – no class –
3	W 1/18	Frequent Itemsets & Association Rules	o Chapter 6 of http://www.mmds.org/#book o Optional: Sections 6.1-6.6 of http://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf
4	M 1/23	Density Estimation	o http://ned.ipac.caltech.edu/level5/March02/Silverman/Silver_contents.html o http://www.stat.washington.edu/courses/stat527/s13/readings/Sheather_StatSci_2004.pdf o Optional: Sections 6.6-6.9 of http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
Homework #1 o out on Monday January 23 o due on Sunday February 5 at 11:59 PM Eastern o grade by Monday February 20
5	W 1/25	Finding Similar Items	o Chapter 3 of http://www.mmds.org/#book
6 7	M 1/30 W 2/1	Mining Data Streams	o Chapter 4 of http://www.mmds.org/#book
8 9	M 2/6 W 2/8	Dimensionality Reduction (PCA, SVD, CUR, Kernel PCA)	o Chapter 11 of http://www.mmds.org/#book o Section 14.5 of http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf o https://alex.smola.org/papers/1999/MikSchSmoMuletal99.pdf
Homework #2 o out on Wednesday February 8 o due on Tuesday February 21 at 11:59 PM Eastern o grade by Monday March 13
10 11	M 2/13 W 2/15	Clustering: K-means, Gaussian Mixture Models, Expectation Maximization (EM)	o Chapter 9 of http://robotics.stanford.edu/~nilsson/MLBOOK.pdf o Sections 7.1-7.3 of http://www.mmds.org/#book o Chapter 8 of https://www.cs.cornell.edu/jeh/book2016June9.pdf o Section 14.3 of http://statweb.stanford.edu/~tibs/ElemStatLearn o http://cs229.stanford.edu/notes/cs229-notes7b.pdf o http://cs229.stanford.edu/notes/cs229-notes8.pdf o Optional: https://www.cs.rutgers.edu/~mlittman/courses/lightai03/jain99data.pdf o Optional: http://web.itu.edu.tr/sgunduz/courses/verimaden/paper/validity_survey.pdf o Optional: http://www.dbs.ifi.lmu.de/Publikationen/Papers/KDD-96.final.frame.pdf
–	M 2/20	President’s Day – no class –
12 13	W 2/22 M 2/27	EM, K-mediods, Hierarchical Clustering, Evaluation Metrics and Practical Issues	o Same readings as for 2/13 and 2/15
14	W 3/1	Midterm Exam (in-class)	Grade by Monday March 13
–	M 3/06 W 3/08	Spring Break – no class –
15	M 3/13	Project Proposal Pitches + Review of Midterm
16	W 3/15	Spectral Clustering	o http://ai.stanford.edu/~ang/papers/nips01-spectral.pdf o http://www.cs.columbia.edu/~jebara/4772/papers/Luxburg07_tutorial.pdf
17	M 3/20	Link Analysis	o Chapter 5 of http://www.mmds.org/#book o Optional: http://bit.ly/2iYxo82
18	W 3/22	Recommendation Systems	o Chapter 9 of http://www.mmds.org/#book
19	M 3/27	Matrix Factorization	o Chapter 14.6 of http://statweb.stanford.edu/~tibs/ElemStatLearn/ o http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf o Optional: http://www.sandia.gov/~tgkolda/pubs/pubfiles/TensorReview.pdf
Homework #3 o out on Monday March 27 o due on Sunday April 9at 11:59 PM Eastern o grade by Monday April 24
20 21	W 3/29 M 4/3	Topic Models	o http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf o http://www.cs.princeton.edu/~blei/papers/Blei2011.pdf o http://www.cs.columbia.edu/~blei/papers/BleiLafferty2009.pdf
22	W 4/5	Hidden Markov Models	o http://bit.ly/2iYxInD o http://digital.cs.usu.edu/%7Ecyan/CS7960/hmm-tutorial.pdf
23	M 4/10	Model Selection (BIC, MDL) Theory of Clustering	o Sections 7.7-7.8 of http://statweb.stanford.edu/~tibs/ElemStatLearn/ o Section 8.12 of https://www.cs.cornell.edu/jeh/book2016June9.pdf o http://www.cs.cornell.edu/home/kleinber/nips15.pdf o http://bit.ly/2j278xP
24	W 4/12	Final Exam (in-class)	Grade by Sunday April 30
–	M 4/17	Patriot’s Day – no class –
25	W 4/19	Project Presentations (in-class)	Last class day
Project reports o due on Wednesday April 26 at 11:59 PM o grade by Sunday April 30
Final grades deadline on Monday May 1 at 9:00 AM

Notes, Policies, and Guidelines

o We will use Northeastern’s Blackboard for announcements, assignments, and your contributions.

o When emailing me or the TA about the course, begin the subject line with [sp17 cs6220].

o Programming exercises will be accepted in MATLAB, Python, or R.

o Homeworks must be done individually. Late homeworks are accepted up to 2 days after the deadline. A penalty of 20% be charged for each late day.

o The class project can be done either individually or in groups of two.

o For your class project, you can use whatever programming language that you like.

o Any regrading request must be submitted in writing and within one week of the returned material. The request must detail precisely and concisely the grading error.

o Refresh your knowledge of the university's academic integrity policy and plagiarism. There is zero-tolerance for cheating!

o Letter grades will be assigned based on the following grade scale (with numbers rounding up; e.g., 92.4 becomes an A)

A	93–100
A-	90–92
B+	87–89
B	83–86
B-	80–82
C+	77–79
C	73–76
C-	70–72
F	< 70