Simon Rogers, Mark Girolami

A First Course in Machine Learning

• Broschiertes Buch

Produktbeschreibung

The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.

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Produktdetails

• Chapman & Hall/CRC Machine Learning & Pattern Recognition
• Verlag: Taylor & Francis Ltd
• 2 ed
• Seitenzahl: 428
• Erscheinungstermin: 30. Juni 2020
• Englisch
• Abmessung: 234mm x 156mm x 23mm
• Gewicht: 634g
• ISBN-13: 9780367574642
• ISBN-10: 0367574640
• Artikelnr.: 61563052

Autorenporträt

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 holds an honorary professorship in Computer Science at the University of Warwick, is an EPSRC Established Career Fellow (2012 - 2017) and previously an EPSRC Advanced Research Fellow (2007 - 2012). He is also honorary Professor of Statistics at University College London, is the Director of the EPSRC funded Research Network on Computational Statistics and Machine Learning and in 2011 was elected to the Fellowship of the Royal Society of Edinburgh when he was also awarded a Royal Society Wolfson Research

Inhaltsangabe

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. Further Topics in Markov Chain Monte Carlo.
Classification and Regression with Gaussian Processes. Dirichlet Process
models.