A First Course in Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition)

Girolami, Mark Mark Girolami,

ISBN 10: 1439824142 / ISBN 13: 9781439824146

Verlag: Chapman and Hall/CRC -, 2011

Beschreibung

Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.

Bestandsnummer des Verkäufers 6545-9781439824146

Bibliografische Details

• Titel: A First Course in Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
• Verlag: Chapman and Hall/CRC -
• Erscheinungsjahr: 2011
• Sprache: Englisch
• ISBN-10: 1439824142
• ISBN-13: 9781439824146
• Einband: paperback
• Zustand: Very Good

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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.