Simon Rogers, Mark Girolami

A First Course in Machine Learning (eBook, PDF)

• Format: PDF

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

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 427
• Erscheinungstermin: 14. Oktober 2016
• Englisch
• ISBN-13: 9781498738545
• Artikelnr.: 46883576

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.

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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.

Rezensionen

"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process-many other texts omit such details, leaving them as 'an exercise for the reader.' Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."
-David Clifton, University of Oxford, UK

"In my opinion, this is by far the best introduction to Machine Learning. It accomplishes something I would think impossible: it assumes essentially only high school mathematics and no statistics background, and yet, by introducing math, probability and statistics as needed, it manages to do an entirely rigorous introduction to Machine Learning. Proofs are not provided only for very few theorems; the book goes fairly deep and is really enjoyable to read. I told my students that this book will be one of the best investments they have ever made!"
-Aleksandar Ignjatovic, University of New South Wales

"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduct