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A First Course in Machine Learning, Second Edition

| £66.62 | €74.92 | Ca$108.08 | Au$106.66
by Simon Rogers & Mark Girolami
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A First Course in Machine Learning, Second Edition by Simon Rogers & Mark Girolami

"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."
-Daniel Barbara, George Mason University, Fairfax, Virginia, USA

"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing 'just in time' the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."
-Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark

"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength...

Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."
-David Clifton, University of Oxford, UK

"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." 
-Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK

"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning...

The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."
-Guangzhi Qu, Oakland University, Rochester, Michigan, USATo view this DRM protected ebook on your desktop or laptop you will need to have Adobe Digital Editions installed. It is a free software. We also strongly recommend that you sign up for an AdobeID at the Adobe website. For more details please see FAQ 1&2. To view this ebook on an iPhone, iPad or Android mobile device you will need the Adobe Digital Editions app, or BlueFire Reader or Txtr app. These are free, too. For more details see this article.

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Ebook Details
Pages: 427
Size: 134.6 MB
Publisher: Chapman and Hall/CRC
Date published:   2016
ISBN: 9781498738569 (DRM-EPUB)
9781498738545 (DRM-PDF)

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This product is listed in the following categories:

Nonfiction > Computers > Machine Theory
Nonfiction > Business & Economics > Statistics
Nonfiction > Computers > Database Management > Database Mining

These authors have products in the following categories:

Nonfiction > Science > Life Sciences > Genetics & Genomics
Nonfiction > Reference
Nonfiction > Computers
Nonfiction > Business & Economics > Statistics
Nonfiction > Computers > Database Management > Database Mining
Nonfiction > Computers > Machine Theory

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