The seven-week course launches February 5, 2018. This course was developed by over ten MIT faculty members at IDSS. It is specially designed for data scientists, business analysts, engineers, and technical managers looking to learn the latest theories and strategies to harness data.
Developed by 10 MIT faculty members at IDSS, this seven-week course is specially designed for data scientist, business analyst, engineers and technical managers looking to learn the latest theories and strategies to harness data. Next course offered May 7, 2018. Offered by MIT xPRO. More information can be found here
Developed by 10 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins Sept 10, 2018.
Developed by 10 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins Oct. 15, 2018.
Developed by 11 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins Feb 4, 2019.
Developed by 11 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins May 13, 2019.
Developed by 11 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins February 3, 2020.
<br> </br> Interested in starting your data science journey? <a href="https://event.on24.com/eventRegistration/EventLobbyServlet?target=reg20.jsp&referrer=&eventid=2170691&sessionid=1&key=02F897D60682F202E261E07985F9CB92&regTag=&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…
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…
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…
