Kamis, 29 Juli 2010

Free Foundations of Machine Learning

Foundations of Machine Learning
Author: Mehryar Mohri
Edition:
Binding: Hardcover
ISBN: 026201825X
Publisher: The MIT Press
Features:



Foundations of Machine Learning (Adaptive Computation and Machine Learning series)


This graduate-level textbook introduces fundamental concepts and methods in machine learning. Search and download computer ebooks Foundations of Machine Learning (Adaptive Computation and Machine Learning series) for free.
Categories: Machine Theory * Computers. Contributors: Mehryar Mohri - Author. Format: Hardcover. Download Foundations of Machine Learning computer ebooks
It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters la

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Foundations of Machine Learning (Hardcover)


This graduate-level textbook introduces fundamental concepts and methods in machinelearning. It describes several important modern algorithms, provides the theoretical underpinningsof these algorithms, and illustrates key aspects for their application. The authors aim to presentnovel theoretical tools and concepts while giving concise proofs even for relatively advancedtopics. Foundations of Machine Learning fills the need for a general textbookthat also offers theoretical details and an emphasis on proofs. Certain topics that are oftentreated with insufficient attention are discussed in more

Mit Pr 9780262018258 Foundations of Machine Learning By Mohri, Mehryar/ Rostamizadeh, Afshin/ Talwalkar, Ameet Description *Author: Mohri, Mehryar/ Rostamizadeh, Afshin/ Talwalkar, Ameet *Series Title: Adaptive Computation and Machine Learning *Publication Date: 2012/08/17 *Number of Pages: 412 *Binding Type: Hardcover *Language: English *Depth: 1.25 *Width: 7.75 *Height: 9.50 SKU: UBM9780262018258 Payment We accept payment via PayPal, Mastercard, Visa, American Express, Discover and PayPal?s ?

Store Search search Title, ISBN and Author Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh Estimated delivery 3-12 business days Format Hardcover Condition Brand New This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and c

Foundations of Machine Learning, ISBN-13: 9780262018258, ISBN-10: 026201825X



Foundations of Machine Learning Free


It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking
t describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters la

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