An introduction to algorithms for efficiently finding patterns in large-scale data.


Instructor: R. Teal Witter. Please call me Teal.

Class Times: We meet Monday, Tuesday, Wednesday, and Thursday in 75 Shannon St Room 202. The lecture is from 10am to noon and the discussion is from 2 to 3pm.

Office Hours: I will hold office hours in 75 Shannon St Room 221 from 3 to 4pm.

Participation: I expect you to engage in class, ask questions, and make connections. So that I can get a sense of how you’re doing, please fill out this form once per lecture. (You will receive one point per lecture if you answer the form.)

Discussion: Please post all your course related questions on Canvas. If your question reveals your solution to a homework problem, please email me instead.

Assignments: You will receive one problem per class. I expect you to solve the problem with your group during the discussion (I’ll be there to answer questions). Once you have solved the problem, you should write up your solution on your own. In addition, there will be a project on a lecture topic of your choice.

Assignment Work Due Self-grade Due
Problem Set 1 Friday 1/12 Monday 1/15
Problem Set 2 Friday 1/19 Monday 1/22
Problem Set 3 Friday 1/26 Monday 1/29
Problem Set 4 Friday 2/2 Monday 2/5
Project Proposal Monday 1/22
Project Friday 2/2

Resources: This class uses material from Chris Musco’s phenomenal graduate Algorithmic Machine Learning and Data Science course at NYU Tandon. For each class, I will post my typed notes and the handwritten slides we use in class.

Class Topic Notes Slides
Thursday 1/4 Set Size Estimation Notes Slides
Monday 1/8 Frequent Items Notes Slides
Tuesday 1/9 Distinct Elements Notes Slides
Wednesday 1/10 Load Balancing Notes Slides
Thursday 1/11 Concentration Inequalities Notes Slides
Tuesday 1/16 High-Dimensional Geometry Notes Slides
Wednesday 1/17 Dimensionality Reduction Notes Slides
Thursday 1/18 Similarity Estimation Notes Slides
Monday 1/22 Singular Value Decomposition Notes Slides
Tuesday 1/23 Power Method Notes Slides
Wednesday 1/24 Spectral Graph Theory Notes Slides
Thursday 1/25 Sketched Regression Notes Slides
Monday 1/29 Fast JL Transform Notes Slides
Tuesday 1/30 Sparse Recovery Notes Slides