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# A First Course in Machine Learning (Machine Learning & Pattern Recognition) 2nd Edition, Kindle Edition

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"**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

- ISBN-13978-1498738484
- Edition2
- PublisherChapman and Hall/CRC
- Publication date14 October 2016
- LanguageEnglish
- File size172593 KB

## Product description

### Review

"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 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. One of the strengths of the book is its practical approach. An extensive collection of code written in MATLAB/Octave, R, and Python is available from an associated web page that allows the reader to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning. This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning."

―Daniel Ortiz-Arroyo, Aalborg University Esbjerg, Denmark

"**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, Chalmers University, Sweden

"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 prerequisites on math or statistics are minimal and following the content is a fairly easy process. 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

"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

### Review

"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

### Review

### From the Publisher

**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

### About the Author

**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

## Product details

- ASIN : B01N7ZEBK8
- Publisher : Chapman and Hall/CRC; 2 edition (14 October 2016)
- Language : English
- File size : 172593 KB
- Simultaneous device usage : Up to 4 simultaneous devices, per publisher limits
- Text-to-Speech : Not enabled
- Enhanced typesetting : Not Enabled
- X-Ray : Not Enabled
- Word Wise : Not Enabled
- Print length : 427 pages

- Best Sellers Rank: 776,418 in Kindle Store (See Top 100 in Kindle Store)
- 95 in Automation Engineering
- 97 in Robotics & Automation
- 121 in Economic Statistics

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## About the author

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*4.3 out of 5 stars*

### Top review from Australia

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### Top reviews from other countries

*4.0 out of 5 stars*Good but not super easy

*5.0 out of 5 stars*A good taster to Statistical Machine Learning with a great story and enough rigour

The second part of the book is more advanced and covers: Gaussian Processes, MCMC, and Mixture Models.

One of my most favourite things is the explanation of PCA using light and shade as the projection (see the attached image)

*5.0 out of 5 stars*A good taster to Statistical Machine Learning with a great story and enough rigour

Reviewed in the United Kingdom on 12 June 2018

The second part of the book is more advanced and covers: Gaussian Processes, MCMC, and Mixture Models.

One of my most favourite things is the explanation of PCA using light and shade as the projection (see the attached image)

*2.0 out of 5 stars*Too many typos and mistakes even in the second edition...

For example, in the definition of a positive definite matrix A (x^t AX >0) it is not stated that x should be nonzero.

In Chapter 2, they use w and hat(w) and sometimes they call w to be a random variable, sometimes hat(w), always

a mixed up. A considerable effort is needed to correct these typos and mistakes.

*5.0 out of 5 stars*A must as a first book in Machine Learning

*5.0 out of 5 stars*Fast shipping & good textbook