Time: MW 2.50pm  4.30pm
Room: Kariotis Hall 011
JanWillem van de Meent [personal page]
Email:
Phone: +1 617 373 7696
Office Hours: Monday 4.30pm  6.00pm (or by appointment)
Hao Wu [bio]
Email:
Office Hours: Wednesday 4.30pm  6.00pm (or by appointment)
Blackboard: DS 5230 / DS 4420 (Homework problems and Grades)
Piazza: DS 5230 & DS 4420 (Discussion)
Midterm Prep: Topic List, Review Slides
Final Prep: Topic List, Review Slides
This course introduces a range of techniques in unsupervised machine learning and data mining:
This course is designed for MS students in computer science. Lectures will focus on developing a mathematical and algorithmic understanding of the methods commonly employed to solve unsupervised machine learning and data mining problems. Homework problem sets will ask students to implement algorithms and/or work out examples.
Students will also collaborate on a project in which they must complete a data analysis taks from start to finish, including preprocessing of data, analysis, and visualization of results.
CS 5800 or CS 7800, or consent of instructor. Students without this prerequisite should email a CV and transcripts to the instructor. If these materials are acceptable, then the student will be asked to complete the selftest prior to admission to the course.
In addition to the formal requirements, students are expected to have a good working knowledge of calculus, linear algebra, probability, statistics, and algorithms.
This class is not structured to directly follow the outline of a text book. The schedule will list chapters from a number of text books as background reading for each lecture, as well as additional additional materials. Students are expected to read the materials in preparation of each lecture.
[HTF] Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction., Springer 2013. [pdf]
[LRU] Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, Mining of Massive Datasets, Cambridge University Press, 2014 [pdf]
[TSK] PangNing Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, 2005. [ch6, ch8]
[Aggarwal] Charu C. Aggarwal, Data Mining, The Textbook, Springer 2015. [pdf]
The HTF and LRU books are freely available from the authors’ websites. The Aggarwal book is available online to Northeastern students.
The homework in this class will consist of 4 problem sets. Submissions must be made via blackboard by 11.59pm on the due date. Please upload a single ZIP file containing both source code for programming problems as well as PDF files for math problems. Name this zip file:
Please follow the following guidelines:
Math Problems
Please submit math exercises as PDF files (preferably in LaTeX).
Programming Problems
The preferred language for this course is Python. However, you may use any programming language you like, as long as your submission contains clear instructions on how to compile and run the code.
Data File Path: Don’t use absolute path for data files in code. Please add a data folder to your project and refer to it using relative path.
3rd Party Jars: If you are using any 3rd party jar, make sure you attach that to submission.
Clarity: When coding up multiple variants of an algorithm, ensure that your code is properly factored into small, readable and clearly commented functions.
The TAs can deduct points for submissions that do not meet these guidelines at their discretion.
The goal of the project is to gain handson experience with analysis of a dataset of your choice. You should select a problem and a dataset that can be analyzed using methods covered in class. The project should be conducted in groups of 24 people. Each group should work independently, but you are welcome to discuss technical issues on Piazza. Completion of the project will include a project proposal, two milestone project updates, a report, and a review of the project by another team.
Students are expected to attend lectures and actively participate by asking questions. While students are required to complete homework programming exercises individually, helping fellow students by explaining course material is encouraged. At the end of the semester, students will be able to indicate which of their peers most contributed to their understanding of the material, and bonus points will be awarded based on this feedback.
The final grade for this course will be weighted as follows:
Class participation will be used to adjust the final grade upwards at the discretion of the instructor.
Students will be asked to indicate the amount of time spent on each homework, as well as the project. The will also be able to indicate what they think went well, and what they think did not go well. There will also be an opportunity to provide feedback on the class after the midterm exam.
Note: This schedule is subject to change and will be adjusted as needed throughout the semester.
Date 
Lectures 
Homework / Project 
Reading 


Wed Sep 05 
1 
Overview: Unsupervised Learning and Data Mining [slides] 

Mon Sep 10 
2 
Math Review [slides] 
Homework 1 Out 
[CS 229 Linear Algebra Notes], 
Wed Sep 12 
3 
Frequent Itemsets & Association Rules [slides] 
Selftest Due (Fri) 

Mon Sep 17 
4 
Maximum Likelihood, Maximum A Posteriori, Conjugacy [slides] 

Wed Sep 19 
5 
Predictive Distribution, Graphical Models, Exponential Families [slides] 

Mon Sep 24 
6 
Bayesian Regression [slides] 
Homework 2 Out 
Bishop [Ch 1.11.2, Ch 3.13.3], Optional: Rasmussen and Williams [Ch 1, Ch 2, Ch 4] 
Wed Sep 26 
7 
Dimensionality Reduction 1 [slides] 

Mon Oct 01 
8 
Dimensionality Reduction 2 [slides] 
Homework 1 Due (Fri) 

Wed Oct 03 
9 
Clustering 1 [slides] 
Project Teams Due 

Mon Oct 08 
Columbus Day (No Class) 

Wed Oct 10 
10 
Clustering 2 [slides] 
Homework 2 Due (Fri) 

Mon Oct 15 
11 
Clustering 3 [slides] 
Homework 3 Out 

Wed Oct 17 
12 
Clustering 4 [slides] 

Mon Oct 22 
13 
Clustering 5 [slides] 
Project Abstracts Due 

Wed Oct 24 
Midterm Exam 

Mon Oct 29 
14 
Topic Modeling 1 [slides] 

Wed Oct 31 
15 
Topic Modeling 2 [slides] 
Homework 3 Due (Fri) 

Mon Nov 05 
16 
Topic Modeling 3 [slides] 
Homework 4 Out 

Wed Nov 07 
17 
Community Detection 1 [slides] 
[Review by Fortunato], Leskovec Rajaraman & Ullman [Ch 10.110.4] 

Mon Nov 12 
Veteran’s Day (No Class) 
Project Milestone 1 Due 

Wed Nov 14 
18 
Link Analysis 
Homework 4 Due (Fri) 
Leskovec Rajaraman & Ullman [Ch 5] 
Mon Nov 19 
19 
Community Detection 2 [slides] 
[Review by Fortunato], Leskovec Rajaraman & Ullman [Ch 10.110.4] 

Wed Nov 21 
Thanksgiving (No Class) 

Mon Nov 26 
20 
Recommender Systems [slides] 
Project Milestone 2 Due 

Wed Nov 28 
Review 

Mon Dec 03 
Project Presentations 

Wed Dec 05 
(No Class) 
Project Reports Due (Fri) 

Mon Dec 10 
(Start of Exam Week) 

Wed Dec 12 
Final Exam 
Project Revuews Due (Fri) 