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Webinar: Inside the MITx MicroMasters Program in Statistics and Data Science

Devavrat Shah, Karene Chu

<br> </br> Interested in starting your data science journey? <a href=";referrer=&amp;eventid=2170691&amp;sessionid=1&amp;key=02F897D60682F202E261E07985F9CB92&amp;regTag=&amp;sourcepage=register">Register for this special free virtual event.</a> You'll receive a confirmation e-mail with further details about the webinar. <br> </br> Demand for professionals skilled in data, analytics, and machine learning is exploding. A recent report by IBM and Burning Glass states that there will be 364K new job openings in data-driven professions this year in the US alone. Data scientists bring value to organizations across industries because they are able…

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On Using Graph Distances to Estimate Euclidean and Related Distances

Ery Arias-Castro (University of California, San Diego)

Abstract:  Graph distances have proven quite useful in machine learning/statistics, particularly in the estimation of Euclidean or geodesic distances. The talk will include a partial review of the literature, and then present more recent developments on the estimation of curvature-constrained distances on a surface, as well as on the estimation of Euclidean distances based on an unweighted and noisy neighborhood graph. - About the Speaker:  Ery Arias-Castro received his Ph.D. in Statistics from Stanford University in 2004. He then took…

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Naive Feature Selection: Sparsity in Naive Bayes

Alexandre d'Aspremont (ENS, CNRS)

Abstract: Due to its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very large-scale settings. We propose a sparse version of naive Bayes, which can be used for feature selection. This leads to a combinatorial maximum-likelihood problem, for which we provide an exact solution in the case of binary data, or a bound in the multinomial case. We prove that our bound becomes tight as the marginal contribution of additional features decreases. Both binary and…

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