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Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

Foundations of Machine Learning Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar ebook
Publisher: MIT Press
Format: pdf
ISBN: 9780262018258
Page: 432

Oct 17, 2013 - I've been taking a few courses offered on these remarkable resources for learning. Dec 15, 2013 - Machine learning approaches have helped solve other difficult data-based problems. It has provided me with a stepping stone to try my luck in Kaggle competitions, as well as diving deeper into the theoretical foundations of machine learning. Sep 30, 2012 - The book Foundations most resembles is actually Kearns and Vazirani — which is even more computational, and, being from 1994, leaves two decades of active work out of the picture. It also costs about three times (!) as much as Foundations . Technically speaking, a linear regression is a very simple type of machine learning algorithm. Nov 3, 2013 - Pattern Recognition and Machine Learning by Christopher Bishop (Springer); Bayesian Reasoning and Machine Learning by David Barber (Cambridge University Press); Programming Collective Intelligence by Toby Segaran (O'Reilly) Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (MIT Press); Introduction to Machine Learning (Adaptive Computation and Machine Learning) by Ethem Alpaydim (MIT Press). Jul 22, 2013 - As machine learning is a very vast area, we will target specifically the neural networks' aspect of machine learning. About machine learning, I would heartily recommend this book. For the most part, the mathematical foundations of machine learning and artificial intelligence are decades old. Jan 13, 2013 - Mehryar Mohri, Afshin Rostamizadeh , "Foundations of Machine Learning" 2012 | ISBN-10: 026201825X | PDF | 432 pages | 3,4 MB. I can do about one course at a time and this is next in the queue after the Stanford Machine Learning class. While I like Vidyasagar's Learning and Generalization , it's more oriented towards probability and control, and omits contemporary topics like support vector machines, boosting, ranking, and on-line learning.