First Course in Machine Learning

By:

Rogers, Simon [author.]

Contributor(s):

Girolami, Mark [author.]

Material type:

TextLanguage: English Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition seriesPublication details: Boca Raton : CRC Press/Taylor & Franics, 2017.Edition: 2nd edDescription: xxix, 397p. : ill. ; 23 cmISBN:

9781032937908

Subject(s):

Other classification:

D65,8(B):(S:72) Q2;Q7

Summary: A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." —Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade.

Item type:

Textual

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Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Textual	Central Science Library	Central Science Library			D65,8(B):(S:72) Q2;Q7 (Browse shelf(Opens below))					Available			SL1655959

Includes index.

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." —Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade.

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