Algorithmic Aspects of Machine Learning
This is a monograph based on the class "Algorithmic Aspects of Machine Learning" taught at MIT in Fall 2013, Spring 2015 and Fall 2017.
Tag(s): Machine Learning
Publication date: 01 Jan 2018
ISBN-10: n/a
ISBN-13: n/a
Paperback: 249 pages
Views: 3,323
Type: Lecture Notes
Publisher: n/a
License: n/a
Post time: 24 Feb 2021 12:00:00
Algorithmic Aspects of Machine Learning
This is a monograph based on the class "Algorithmic Aspects of Machine Learning" taught at MIT in Fall 2013, Spring 2015 and Fall 2017.
Publication date: 01 Jan 2018
ISBN-10: n/a
ISBN-13: n/a
Paperback: 249 pages
Views: 3,323
Document Type: Lecture Notes
Publisher: n/a
License: n/a
Post time: 24 Feb 2021 12:00:00
Ankur Moitra wrote:In this book, we will approach the problem of giving provable guarantees for machine learning by trying to find more realistic models for our data. In many applications, there are reasonable assumptions we can make based on the context in which the problem came up, that can get us around these worst-case impediments and allow us to rigorously analyze heuristics that are used in practice, as well as design fundamentally new ways of solving some of the central, recurring problems in machine learning.
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About The Author(s)
Ankur Moitra is Associate Professor of Mathematics in the Department of Mathematics at Massachusetts Institute of Technology as of July, 2017. He received tenure in July 2019. He received his B.S. in electrical and computer engineering from Cornell in 2007. He completed his M.S. and Ph.D. degrees from MIT in computer science in 2009 and 2011 respectively, where he was advised by Tom Leighton and was supported by a Fannie and John Hertz Foundation Fellowship. He received a George M. Sprowls Award (best thesis) and a William A. Martin Award (best thesis) for his doctoral and master's dissertations.
Ankur Moitra is Associate Professor of Mathematics in the Department of Mathematics at Massachusetts Institute of Technology as of July, 2017. He received tenure in July 2019. He received his B.S. in electrical and computer engineering from Cornell in 2007. He completed his M.S. and Ph.D. degrees from MIT in computer science in 2009 and 2011 respectively, where he was advised by Tom Leighton and was supported by a Fannie and John Hertz Foundation Fellowship. He received a George M. Sprowls Award (best thesis) and a William A. Martin Award (best thesis) for his doctoral and master's dissertations.