### Resumé

—Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark

Originalsprog | Engelsk |
---|---|

Tidsskrift | A First Course in Machine Learning |

Vol/bind | 2 |

Antal sider | 1 |

Status | Udgivet - 2016 |

### Citer dette

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**Book review: A first course in Machine Learning.** / Ortiz-Arroyo, Daniel.

Publikation: Bidrag til tidsskrift › Anmeldelse › Forskning

TY - JOUR

T1 - Book review: A first course in Machine Learning

AU - Ortiz-Arroyo, Daniel

PY - 2016

Y1 - 2016

N2 - "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. One of the strengths of the book is its practical approach. An extensive collection of code written in MATLAB/Octave, R, and Python is available from an associated web page that allows the reader to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning. This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning."—Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark

AB - "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. One of the strengths of the book is its practical approach. An extensive collection of code written in MATLAB/Octave, R, and Python is available from an associated web page that allows the reader to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning. This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning."—Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark

M3 - Literature review

VL - 2

JO - A First Course in Machine Learning

JF - A First Course in Machine Learning

ER -