Logistics

Instructor

Course Coordinator


Content

What is this course about? [Info Handout]

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, Web Advertising.

Previous offerings

The previous version of the course is CS345A: Data Mining which also included a course project. CS345A has now been split into two courses, CS246 and CS341.

You can access class notes and slides of previous versions of the course here:
CS246 Websites: CS246: Winter 2020 / CS246: Winter 2019 / CS246: Winter 2018 / CS246: Winter 2017 / CS246: Winter 2016 / CS246: Winter 2015 / CS246: Winter 2014 / CS246: Winter 2013 / CS246: Winter 2012 / CS246: Winter 2011
CS345a Website: CS345a: Winter 2010

Prerequisites

Students are expected to have the following background:

The recitation sessions in the first weeks of the class will give an overview of the expected background.

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


Schedule

Lecture slides will be posted here shortly before each lecture. If you wish to view slides further in advance, refer to last year's slides, which are mostly similar.

This schedule is subject to change. All deadlines are at 11:59pm PST.

Date Description Course Materials Events Deadlines
Tue March 30 Introduction; MapReduce and Spark
[slides]
Suggested Readings:
  1. Chapter 1: Data Mining
  2. Chapter 2: Large-Scale File Systems and Map-Reduce
Thu April 1 Frequent Itemsets Mining
[slides]
Suggested Readings:
  1. Ch6: Frequent itemsets
Colab 0,
Colab 1,
Homework 1
out
Recitation: Spark tutorial
[Colab]
Tue April 6 Locality-Sensitive Hashing I
[slides]
Suggested Readings:
  1. Ch3: Finding Similar Items (Sect. 3.1-3.4)
Thu April 8 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
Recitation: Probability and Proof Techniques
[handout]
Recitation: Linear Algebra
[handout]
Tue April 13 Clustering
[slides]
Suggested Readings:
  1. Ch7: Clustering (Sect. 7.1-7.4)
Thu April 15 Dimensionality Reduction
[slides]
Suggested Readings:
  1. Ch11: Dimensionality Reduction (Sect. 11.4)
Colab 3,
Homework 2
out
Colab 2,
Homework 1 due
Tue April 20 Recommender Systems I
[slides]
Suggested Readings:
  1. Ch9: Recommendation systems
Thu April 22 Recommender Systems II
[slides]
Suggested Readings:
  1. Ch9: Recommendation systems
Colab 4
out
Colab 3
due
Tue April 27 PageRank
[slides]
Suggested Readings:
  1. Ch5: Link Analysis (Sect. 5.1-5.3, 5.5)
Thu April 29 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,
Homework 3
out
Colab 4,
Homework 2 due
Tue May 4 Community Detection in Graphs
[slides]
Suggested Readings:
  1. Ch10: Analysis of Social Networks (Sect. 10.3-10.5)
Thu May 6 Graph Representation Learning
[slides]
Suggested Readings:
  1. Ch10: Analysis of Social Networks (Sect. 10.7-10.8)
Colab 6
out
Colab 5
due
Tue May 11 Large-Scale Machine Learning I
[slides]
Suggested Readings:
  1. Ch12: Large-Scale Machine Learning
Thu May 13 Large-Scale Machine Learning II
[slides]
Suggested Readings:
  1. Ch12: Large-Scale Machine Learning
Colab 7,
Homework 4
out
Colab 6,
Homework 3 due
Tue May 18 Mining Data Streams I
Suggested Readings:
  1. Ch4: Mining data streams (Sect. 4.1-4.3)
Thu May 20 Mining Data Streams II
Suggested Readings:
  1. Ch4: Mining data streams (Sect. 4.4-4.7)
Colab 8
out
Colab 7
due
Tue May 25 Computational Advertising
Suggested Readings:
  1. Ch8: Advertising on the Web
Thu May 27 Learning through Experimentation
Suggested Readings:
  1. A Contextual-Bandit Approach to Personalized News Article Recommendation by Li, Chu, Langford, Schapier. WWW 2010.
Colab 9
out
Colab 8,
Homework 4 due
Tues Jun 1
Optimizing Submodular Functions
Suggested Readings:
  1. Turning Down the Noise in the Blogosphere by El-Arini, Veda, Shahaf, Guestrin. KDD 2009.
Thu Jun 3 TBD Colab 9
due