A First Course in Machine Learning

by Simon Rogers, Mark Girolami

" 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

Genres: Artificial Intelligence

428 pages, Paperback

First published October 25, 2011

About the author

Simon 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.

Reviews

[Charlie · Rating 5 out of 5]

An excellent introduction to the concepts of machine learning which builds up from first principles. I was fortunate enough to have attended the university course run by Simon Rogers who is an excellent teacher and also a lovely human being. This book requires little mathematical background to start with and introduces all of the necessary linear algebra, calculus, and statistics in a clear and concise way as the book develops and complexity increases.
After learning these ML concepts it's also great as a reference to particular ideas and ML algorithms. However, it is rather light on the actual programming side of things in the text, but there are exercises in maths and coding provided for free alongside to allow you to practice and implement the concepts taught in the book. Highly recommended!

[B R · Rating 3 out of 5]

I think this book might very well have saved me from failing my statistical machine learning course! They introduce all the mathematical concepts you need (results from linear algebra, vector calculus, and the properties of multivariate Gaussians) as the need arises, which makes it far far easier to remember/internalise the relevant mathematics. Additionally there are beautiful explanations of Gaussian Processes and famous sampling algorithms like Metropolis-Hastings. The only downside is that the authors claim that there is python code available for the whole book on their website, but when I went there to take a look, all I saw was yucky R and Matlab code. Still, a very good resource for statistical ML, particularly the Bayesian approach, and for the mathematics and ML concepts needed to dive into deep learning.