A First Course in Machine Learning: 1st Edition (e-Book) book cover

A First Course in Machine Learning

1st Edition

By Mark Girolami

Chapman and Hall/CRC

305 pages

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eBook (VitalSource) : 9780429195419
pub: 2011-10-25
from $38.48


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Description

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 smal

Table of Contents

Linear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Glossary. Index.

About the Author

Simon Rogers is a lecturer in the School of Computing Science at the University of Glasgow, where he teaches a masters-level machine learning course on which this book is based. Dr. Rogers is an active researcher in machine learning, particularly applied to problems in computational biology. His research interests include the analysis of metabolomic data and the application of probabilistic machine learning techniques in the field of human-computer interaction.

Mark Girolami is a chair of statistics and an honorary professor of computer science at University College London, where he is also the director of the Centre for Computational Statistics and Machine Learning. An EPSRC Advanced Research Fellow, an IET Fellow, and a Fellow of the Royal Society of Edinburgh, Dr. Girolami has made major contributions to the field, including his generalisation of independent component analysis, his work on inference in systems biology, and his innovations in statistical methodology.

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM000000
COMPUTERS / General
COM012040
COMPUTERS / Programming / Games
COM021030
COMPUTERS / Database Management / Data Mining