A first course in machine learning / Simon Rogers, University of Glasgow, United Kingdom, Mark Girolami, University of Warwick, United Kingdom.
Series: Chapman & Hall / CRC Machine Learning & Pattern Recognition SeriesCopyright date: Boca Raton, Florida : CRC Press, is an imprint of the Taylor & Francis Group, c2017Edition: Second editionDescription: xxix, 397 pages ; 25 cmContent type:- text
- unmediated
- volume
- 9781498738484
- 006.31 .R729 2017
- Q325.5 .R64 2017
Item type | Current library | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|
Print Materials | Main Library General Circulation | 006.31 .R729 2017 (Browse shelf(Opens below)) | Available | 0122372 |
Browsing Main Library shelves, Shelving location: General Circulation Close shelf browser (Hides shelf browser)
006.31 .Al456 2021 Machine learning / | 006.31 .F981 2018 Fundamentals of machine learning / | 006.31 .M697 2017 Machine learning : | 006.31 .R729 2017 A first course in machine learning / | 006.312 .B618 2015 The art and science of analyzing software data / | 006.312 .D232 2017 Data mining / | 006.312 .Ig24 2018 An introduction to text mining : |
"A Chapman & Hall Book" --Cover
Includes bibliographical references and index
"A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail.
Referenced throughout the text and available on a supporting website (http://bit.ly/firstcourseml), an extensive collection of MATLAB®/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems.
Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail." --Amazon website
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