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Material Detail

18.S096 Topics in Mathematics of Data Science | Mathematics

18.S096 Topics in Mathematics of Data Science | Mathematics

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.... Show More
Keywords:
Cheeger?s inequality, Stochastic Block Model, Mesh Theorem, Probability and Statistics, manifold learning, inverse problems, Synchronization, Approximation algorithms, random matrix theory, Diffusion Maps, Sobolev Embedding Theorem, Max-Cut problem, Principal Component Analysis (PCA), Spectral Clustering, Number Theory, Applied Mathematics, spike model
Disciplines:
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More about this material

Material Type:
Online Course
Date Added to MERLOT:
May 12, 2016
Date Modified in MERLOT:
May 12, 2016
Author:
Unknown (Know the author?)
Submitter:
Barbra Bied Sperling
Hispanic Serving Institution
Primary Audience:
College General Ed, College Lower Division, College Upper Division
Technical Format:
Website
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
No
Creative Commons:
Creative Commons License
This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States

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