18.S096 | Fall 2015 | Undergraduate
Topics in Mathematics of Data Science


1 Overview and Two Open Problems  
2-4 Principal Component Analysis in High Dimensions and the Spike Model  
5-7 Graphs, Diffusion Maps, and Semi-supervised Learning  
8-11 Spectral Clustering and Cheeger’s Inequality Problem Set 1 due
12-14 Concentration Inequalities, Scalar and Matrix Versions Problem Set 2 due
15-16 Johnson-Lindenstrauss Lemma and Gordon’s Theorem Problem Set 3 due
17 Local Convergence of Graphs and Enumeration of Spanning Trees  
18-19 Compressed Sensing and Sparse Recovery Project Abstract due
20 Group Testing and Error-Correcting Codes Problem Set 4 due
21 Approximation Algorithms and Max-Cut  
22 Community Detection and the Stochastic Block Model Problem Set 5 due
23 Synchronization Problems and Alignment  
24 Project Presentations Project Presentations
25 Project Report Project Report due
Course Info
As Taught In
Fall 2015
Learning Resource Types
assignment Problem Sets
notes Lecture Notes
co_present Instructor Insights