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Created by:
Last updated:
December 27, 2022
Duration:
Unlimited Duration
FREE
This course includes:
Unlimited Duration
Badge on Completion
Certificate of completion
Unlimited Duration
Description
This is a mostly self-contained research-oriented course designed for undergraduate students (but also extremely welcoming to graduate students) with an interest in doing research in theoretical aspects of algorithms that aim to extract information from data.
These often lie in overlaps of two or more of the following: Mathematics, Applied Mathematics, Computer Science, Electrical Engineering, Statistics, and / or Operations Research.
Course Curriculum
- Overview and Two Open Problems Unlimited
- Principal Component Analysis in High Dimensions and the Spike Model Unlimited
- Graphs, Diffusion Maps, and Semi-supervised Learning Unlimited
- Spectral Clustering and Cheeger’s Inequality Unlimited
- Concentration Inequalities, Scalar and Matrix Versions Unlimited
- Johnson-Lindenstrauss Lemma and Gordon’s Theorem Unlimited
- Local Convergence of Graphs and Enumeration of Spanning Trees Unlimited
- Compressed Sensing and Sparse Recovery Unlimited
- Group Testing and Error-Correcting Codes Unlimited
- Approximation Algorithms and Max-Cut Unlimited
- Community Detection and the Stochastic Block Model Unlimited
- Synchronization Problems and Alignment Unlimited
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Massachusetts Institute of Technology
