Logistics

Instructor


Teaching Assistants


Ashish Sharma
(Head TA)
Kristof Glauninger
 
Qifan Huang
 
Stephen J. Jonany
 
Jack Khuu
 
Alon Milchgrub
 
Galen Weld
 
Chi-Hui Yen
 

Content

What is this course about?

The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on MapReduce and Spark as tools for creating parallel algorithms that can process very large amounts of data.
Topics include: Frequent itemsets and Association rules, Near Neighbor Search in High Dimensional Data, Locality Sensitive Hashing (LSH), Dimensionality reduction, Recommendation Systems, Clustering, Link Analysis, Large scale supervised machine learning, Data streams, Mining the Web for Structured Data.

This course is modeled after CS246: Mining Massive Datasets by Jure Leskovec at Stanford University.

Reference Text

The following text is useful, but not required. It can be downloaded for free, or purchased from Cambridge University Press.
Leskovec-Rajaraman-Ullman: Mining of Massive Dataset

Prerequisites

Students are expected to have the following background:

Students may refer to the following materials for an overview and review of the expected background. Related questions can be posted on EdDiscussion or during Office Hours.

Recordings of the recitations are available on Panopto. You will need to login using your UW netid in order to watch the videos. Note: the audio might be a little off, particularly in the Spark recitation video, due to technical difficulties.

Students may decide to enroll without knowledge of these prerequisites but expect an significant increase in work load to learn these concurrently (e.g. 10 hours per week per missing prerequisite as a rule of thumb).


Schedule

Note: Lectures will be conducted via Zoom. Links posted on Canvas.

Lecture slides will be posted here shortly before each lecture.

This schedule is subject to change.

Date Description Course Materials Events Deadlines
Tue Mar 31 Introduction; MapReduce and Spark
[slides]
Course Information: handout

Suggested Readings:
  1. Ch1: Data Mining
  2. Ch2: Large-Scale File Systems and Map-Reduce
Start planning course project
[Teams signup form]
Thu Apr 2 Frequent Itemsets Mining
[slides]
Suggested Readings:
  1. Ch6: Frequent itemsets
Colab 0, Colab 1
[Colab 0] [Colab 1] & Assignment 1 out
[handout, bundle file]
Thu Apr 2 Recitation: Spark

1:00-3:00pm via Zoom
Tue Apr 7 Locality-Sensitive Hashing I
[slides]
Suggested Readings:
  1. Ch3: Finding Similar Items (Sect. 3.1-3.4)
Tue Apr 7 Recitation: Probability and Proof Techniques
3:30-5:30pm via Zoom
Thu Apr 9 Locality-Sensitive Hashing II
[slides]
Suggested Readings:
  1. Ch3: Finding Similar Items (Sect. 3.5-3.8)
[Colab 2] out Colab 0, Colab 1 due
Thu Apr 9 Recitation: Linear Algebra
[notes] taken from 2019
1:00-3:00pm via Zoom
Tue Apr 14 Clustering
[slides]
Suggested Readings:
  1. Ch7: Clustering (Sect. 7.1-7.4)
Thu Apr 16 Dimensionality Reduction
[slides]
Suggested Readings:
  1. Ch11: Dimensionality Reduction (Sect. 11.4)
[Colab 3] & Assignment 2 out
[handout, bundle file]
Colab 2
& Assignment 1 due
Tue Apr 21 Recommender Systems I
[slides]
Suggested Readings:
  1. Ch9: Recommendation systems
Thu Apr 23 Recommender Systems II
[slides]
Suggested Readings:
  1. Ch9: Recommendation systems
[Colab 4] out
Colab 3 due,
Project Proposal due (no late periods)
Tue Apr 28 PageRank
[slides]
Suggested Readings:
  1. Ch5: Link Analysis (Sect. 5.1-5.3, 5.5)
Thu April 30 Link Spam and Introduction to Social Networks
[slides]
Suggested Readings:
  1. Ch5: Link Analysis (Sect. 5.4)
  2. Ch10: Analysis of Social Networks (Sect. 10.1-10.2, 10.6)
[Colab 5]
& Assignment 3 out
[handout, bundle file]
Colab 4
& Assignment 2 due
Tue May 5 Community Detection in Graphs
[slides]
Suggested Readings:
  1. Ch10: Analysis of Social Networks (Sect. 10.3-10.5)
Thu May 7 Graphs Representation Learning
[slides]
Suggested Readings:
  1. Ch10: Analysis of Social Networks (Sect. 10.7-10.8)
[Colab 6] out
Colab 5 due,
Project Milestone due on Sunday (May 10) midnight (no late periods)
Tue May 12 Large-Scale Machine Learning I
[slides]
Suggested Readings:
  1. Ch12: Large-Scale Machine Learning
Thu May 14 Large-Scale Machine Learning II
[slides]
Suggested Readings:
  1. Ch12: Large-Scale Machine Learning
[Colab 7]
& Assignment 4 out
[handout, bundle file]
Colab 6
& Assignment 3 due
Tue May 19 Mining Data Streams I
[slides]
Suggested Readings:
  1. Ch4: Mining data streams (Sect. 4.1-4.3)
Thu May 21 Mining Data Streams II
[slides]
Suggested Readings:
  1. Ch4: Mining data streams (Sect. 4.4-4.7)
[Colab 8] out
Colab 7 due
Tue May 26 Course Project Meetings (optional)
Sign up for meeting slots on EdDiscussion
Thu May 28 Optimizing Submodular Functions
Suggested Readings:
  1. TimeMachine: Timeline Generation for Knowledge-base Entities by Althoff, Dong, Murphy, Alai, Dang, Zhang. KDD 2015.
Colab 9 out
Assignment 4 due
Tue Jun 2 Data Science I
Colab 8 due
Thu Jun 4 Data Science II
Sun Jun 7 Colab 9 due
& Final Report due & Presentation video due (no late periods)
Mon Jun 8 10:00am-1:00pm Virtual Project Presentations