Fall 2018: CS6220 Data Mining Techniques,
Section 01, CRN 12896
General
Information
Lecture time: Tuesdays & Fridays
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Place: Behrakis Health
Sciences Center, Room 320
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Instructor: Tina Eliassi-Rad
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Office hours: Tuesdays 5:30 – 6:30 PM in West Village H (WVH), Room 362
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TA: Zikun Lin & Hui “Sophie” Wang
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TAs office hours:
· Zikun Lin:
Wednesdays 3:00 – 4:30 PM in Nightingale Hall, Room 132i
· Sophie Wang: Thursdays
4:00 – 5:30 PM in Ryder Hall, Room 273
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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) (Errata)
- 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
- Mathworks Matlab Tutorials
- Ben Taskar's Matlab Tutorial
- Probability Review (David Blei, Princeton)
- Probability Theory Review (Arian Maleki and Tom Do, Stanford)
- Linear Algebra Tutorial (C.T. Abdallah, Penn)
- Linear Algebra Review and Reference (Zico Kolter and Chuong Do, Stanford)
- Probability, Linear Algebra, and Differentiation (Iain Murray)
- Statistical Data Mining Tutorials (Andrew Moore, Google/CMU)
- Theoretical CS Cheat Sheet (Princeton)
- Encyclopedia of Distances (Michel Marie Deza and Elena Deza)
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)
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Lec # |
Date |
Topic |
Readings &
Notes |
|
1 |
F 9/7 |
Introduction and Overview |
o Chapter
1 of http://eliassi.org/mmds-book-v2L.pdf
|
|
2 |
T 9/11 |
Review of Linear
Algebra (Guest lecturer: |
o Linear Algebra Tutorial (C.T. Abdallah, Penn) o Linear Algebra Review and Reference (Zico Kolter and Chuong Do, Stanford) o Probability Review (David Blei, Princeton) o Probability Theory Review (Arian Maleki and Tom Do, Stanford) |
|
3 |
F 9/14 |
Social Bots (Guest lecturer: |
o https://arxiv.org/abs/1407.5225 |
|
4 |
T 9/18 |
Frequent Itemsets & Association Rules |
o Chapter
6 of http://eliassi.org/mmds-book-v2L.pdf o Optional:
Sections 6.1-6.6 of http://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf
|
|
5 |
F 9/21 |
Frequent Itemsets & Association Rules |
o Chapter
6 of http://eliassi.org/mmds-book-v2L.pdf o Optional:
Sections 6.1-6.6 of http://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf
|
|
6 |
T 9/25 |
Density Estimation |
o http://ned.ipac.caltech.edu/level5/March02/Silverman/Silver_contents.html
o http://eliassi.org/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 Tuesday September 25 o
due on Friday October 5 at 11:59
PM Eastern o grade
by Friday October 19 |
|||
|
7 |
F 9/28 |
Finding Similar Items |
o Chapter
3 of http://eliassi.org/mmds-book-v2L.pdf |
|
8 9 |
T 10/2 F 10/5 |
Mining Data Streams |
o Chapter
4 of http://eliassi.org/mmds-book-v2L.pdf |
|
10 11 |
T 10/9 F 10/12 |
Dimensionality
Reduction (PCA, SVD, CUR, |
o Chapter
11 of http://eliassi.org/mmds-book-v2L.pdf o
Section 14.5 of http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
|
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Homework #2 o out
on Friday October 12 o due
on Tuesday October 23 at 11:59 PM Eastern o grade
by Tuesday November 6 |
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12 13 |
T 10/16 F 10/19 |
Clustering: |
o Chapter
9 of http://robotics.stanford.edu/~nilsson/MLBOOK.pdf
o Sections
7.1-7.3 of http://eliassi.org/mmds-book-v2L.pdf 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
|
|
14 |
F 10/23 |
EM, K-mediods, Hierarchical Clustering, Evaluation Metrics and Practical Issues |
o Same
readings as for 10/16 & 10/19 |
|
15 |
T 10/26 |
Spectral Clustering |
o http://ai.stanford.edu/~ang/papers/nips01-spectral.pdf o http://www.cs.columbia.edu/~jebara/4772/papers/Luxburg07_tutorial.pdf |
|
16 |
T 10/30 |
Midterm Exam |
Grade by Tuesday November 13 |
|
17 |
F 11/2 |
Project Proposal Pitches |
|
|
18 |
T 11/6 |
Link Analysis |
o Chapter
5 of http://eliassi.org/mmds-book-v2L.pdf o Optional:
http://bit.ly/2iYxo82 |
|
19 |
F 11/9 |
Recommendation Systems |
o Chapter
9 of http://eliassi.org/mmds-book-v2L.pdf |
|
20 |
T 11/13 |
Recommendation Systems |
o Chapter
9 of http://eliassi.org/mmds-book-v2L.pdf |
|
21 |
F 11/16 |
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
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Homework #3 o out
on Friday November 16 o
due on Tuesday November 27 at
11:59 PM Eastern o grade
by Tuesday December 11 |
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22 |
T 11/20 |
Topic Models |
o http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf
o http://www.cs.columbia.edu/~blei/papers/Blei2011.pdf
o http://www.cs.columbia.edu/~blei/papers/BleiLafferty2009.pdf
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|
23 |
F 11/23 |
Thanksgiving Break --- no class --- |
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24 |
T 11/27 |
Hidden Markov Models & Review for Final |
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|
25 |
F 11/30 |
Final Exam |
o Grade
by Friday December 14 |
|
26 |
T 12/4 |
Project Presentations: Group 1 |
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|
27 |
F 12/7 |
Project Presentations: Group 2 |
Last class day |
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Project reports o due
on Tuesday December 11 at 11:59 PM o grade
by Sunday December 16 |
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Final grades are due to the Registrar Office on Monday December 17 at 2:00 PM Eastern. |
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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 [fa18 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 48 hours 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)
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A |
93-100 |
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A- |
90-92 |
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B+ |
87-89 |
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B |
83-86 |
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B- |
80-82 |
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C+ |
77-79 |
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C |
73-76 |
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C- |
70-72 |
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F |
< 70 |