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A First Course in Machine Learning, Second Edition
A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC.
--Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden
This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade.
--Daniel Barbara, George Mason University, Fairfax, Virginia, USA
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.
--Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark
I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength...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
The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book.
--Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK
This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning...The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective.
--Guangzhi Qu, Oakland University, Rochester, Michigan, USA
--Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden
This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade.
--Daniel Barbara, George Mason University, Fairfax, Virginia, USA
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.
--Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark
I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength...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
The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book.
--Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK
This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning...The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective.
--Guangzhi Qu, Oakland University, Rochester, Michigan, USA
397 pages, ebook
Published October 14, 2016
4 people are currently reading
18 people want to read
About the author
Simon Rogers
48 books6 followersSimon Rogers is the founding editor of the Guardian’s Datablog and has won numerous awards for his work, including a Royal Statistical Society’s award of excellence in 2012.
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Displaying 1 - 2 of 2 reviews
January 30, 2022
A truly hidden gem. I found this book by chance hidden in a reference section in some Monte Carlo Markov Chain notebook somewhere.
The book really delivers its message, as a "First" course in Machine Learning, with a Bayesian flavor. While it does not shy away from math, it's (mostly) readable.
The book really delivers its message, as a "First" course in Machine Learning, with a Bayesian flavor. While it does not shy away from math, it's (mostly) readable.
June 14, 2018
I liked it. It started out well, chapter 1 and 2 being nice and comfortable. Then 3, 4, 5, 6 and 7 got gradually more advanced. I have implemented at least one of the included ML algorithms for each chapter (in Python) and I learned a ton from doing this. Then I read the details in the book and got a little more confused. But that is OK; there is a lot of math here, and not all of it is helpful for absorbing the content. The advanced chapters 8, 9 and 10 got pretty serious and I understood very little.
Displaying 1 - 2 of 2 reviews