Spring 2019: CS 6220  Data Mining
Techniques, CRN 33181 crosslisted with
Spring 2019: DS 5230  Unsupervised
Machine Learning and Data Mining, CRN 34643
Lecture time: Tuesdays & Fridays

Place: International
Village, Room 019

Instructor: Tina EliassiRad

Office hours: Tuesdays 3:30 – 5:00 PM in International
Village, Room 016

TA: Deeksha Doddahonnaiah

Office hours:
Thursdays 3:30 – 5:00 PM in West
Village F, Room 118
Also, available by appointment. Email doddahonnaiah.d [at] husky [dot]
neu [dot] edu; begin the subject line with [sp19
dm].

TA: Hui “Sophie” Wang

Office hours:
Wednesdays 10:00 – 11:30 AM in Hastings
Hall at the YMCA, Room 105
Also, available by
appointment. Email wang.hui1 [at] husky [dot] neu [dot] edu; begin the
subject line with [sp19 dm].

This 4credit graduatelevel course covers data mining and unsupervised learning. Its prerequisites are:
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.
Lec # 
Date 
Topic 
Readings &
Notes 
1 
T 1/8 
Introduction and Overview 
o Chapter
1 of http://eliassi.org/mmdsbookv2L.pdf
o http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf

2 
F 1/11 
Review of Linear
Algebra 
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 
T 1/15 
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 6.66.9 of http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf

4 
F 1/18 
Frequent Itemsets & Association Rules 
o Chapter
6 of http://eliassi.org/mmdsbookv2L.pdf o Optional:
Sections 6.16.6 of http://wwwusers.cs.umn.edu/~kumar/dmbook/ch6.pdf

5 
T 1/22 
Frequent Itemsets & Association Rules 
o Chapter
6 of http://eliassi.org/mmdsbookv2L.pdf o Optional:
Sections 6.16.6 of http://wwwusers.cs.umn.edu/~kumar/dmbook/ch6.pdf

Homework #1 o out
on Tuesday January 22 o due
on Friday February 1 at 11:59 PM Eastern o graded by Friday February 15 

6 
F 1/25 
Finding Similar Items 
o Chapter
3 of http://eliassi.org/mmdsbookv2L.pdf 
7 
T 1/29 
Finding Similar Items 
o Chapter
3 of http://eliassi.org/mmdsbookv2L.pdf 
8 
F 2/1 
Mining Data Streams 
o Chapter
4 of http://eliassi.org/mmdsbookv2L.pdf 
9 
T 2/5 
Mining Data Streams 
o Chapter
4 of http://eliassi.org/mmdsbookv2L.pdf 
10 
F 2/8 
Mining Data Streams 
o Chapter
4 of http://eliassi.org/mmdsbookv2L.pdf 
Homework #2 o out
on Friday February 8 o due
on Monday February
18 at 11:59 PM Eastern o graded
by Friday March 1 

11 12 
T 2/12 F 2/15 
Dimensionality
Reduction (PCA, SVD, CUR, 
o Chapter
11 of http://eliassi.org/mmdsbookv2L.pdf o
Section 14.5 of http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf

13 14 
T 2/19 F 2/22 
Clustering: 
o Chapter
9 of http://robotics.stanford.edu/~nilsson/MLBOOK.pdf
o Sections
7.17.3 of http://eliassi.org/mmdsbookv2L.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/cs229notes7b.pdf o http://cs229.stanford.edu/notes/cs229notes8.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/KDD96.final.frame.pdf

Homework #3 o out
on Tuesday February 19 o
due on Friday March 1 at 11:59 PM
Eastern o graded by Friday March 15 

15 
T 2/26 
EM, Kmediods, Hierarchical Clustering, Evaluation Metrics and Practical Issues 
o Same
readings as for 10/16 & 10/19 
16 
F 3/1 
Midterm Exam 
Graded by Tuesday March 19 
 
T 3/5 F 3/8 
Spring Break 

17 
T 3/12 
Project Proposal Pitches (inclass) 
Graded by Tuesday March 19 
18 19 
F 3/15 T 3/19 
Spectral Clustering 
o http://ai.stanford.edu/~ang/papers/nips01spectral.pdf o http://www.cs.columbia.edu/~jebara/4772/papers/Luxburg07_tutorial.pdf 
Homework #4 o out
on Tuesday March 19 o
due on Friday March 29 at 11:59 PM
Eastern o graded by Friday April 12 

20 
F 3/22 
Recommendation Systems 
o Chapter
9 of http://eliassi.org/mmdsbookv2L.pdf 
21 
T 3/26 
Recommendation Systems 
o Chapter
9 of http://eliassi.org/mmdsbookv2L.pdf 
22 
F 3/29 
Matrix Factorization 
o Chapter
14.6 of http://statweb.stanford.edu/~tibs/ElemStatLearn/ o
http://papers.nips.cc/paper/1861algorithmsfornonnegativematrixfactorization.pdf
o Optional:
http://www.sandia.gov/~tgkolda/pubs/pubfiles/TensorReview.pdf

Homework #5 o out
on Friday March 29 o
due on Tuesday April 9 at 11:59 PM
Eastern o graded by Tuesday April 16 

23 
T 4/2 
Social Bots (Guest lecturer: 
o https://arxiv.org/abs/1407.5225 
24 
F 4/5 
Link Analysis 
o Chapter
5 of http://eliassi.org/mmdsbookv2L.pdf o Optional: http://bit.ly/2iYxo82

25 
T 4/9 
Review for Final 

26 
F 4/12 
Final Exam 
Graded by Friday April 26 
27 
T 4/16 
Project Presentations: Group 1 

28 
F 4/19 
Project Presentations: Group 2 
Last class day 
Project reports o due
on Tuesday April 23 at 11:59 PM o graded
by Sunday April 28 

Final grades are due to the
Registrar Office on Monday April 29 at 9:00 AM Eastern. 
A 
93100 
A 
9092 
B+ 
8789 
B 
8386 
B 
8082 
C+ 
7779 
C 
7376 
C 
7072 
F 
< 70 