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A First Course in Machine Learning (Machine Learning & Pattern Recognition) by [Simon Rogers, Mark Girolami]

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

4.3 out of 5 stars 28 ratings

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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

--This text refers to an alternate kindle_edition edition.

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

--This text refers to an alternate kindle_edition edition.

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
  • Customer Reviews:
    4.3 out of 5 stars 28 ratings

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4.3 out of 5 stars
4.3 out of 5
28 global ratings

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Top review from Australia

Reviewed in Australia on 12 October 2020
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ZigZag
4.0 out of 5 stars Good but not super easy
Reviewed in the United Kingdom on 28 August 2017
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3 people found this helpful
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Jan Povala
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 13 June 2018
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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
This book is one of the few technical books that I've read cover to cover. One of the strongest points of the book is that it provides a story. Starting with Least Squares method applied to Olympic 100m dataset, moving on to Maximum Likelihood Estimation, Bayesian regression and Bayesian inference in general, all the way to more complicated methods such as classification, clustering, PCA. All these topics are applied to the same datasets so the reader can see a good comparison of the methods. Most importantly, very little is assumed about reader's maths abilities.

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)
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just_me
2.0 out of 5 stars Too many typos and mistakes even in the second edition...
Reviewed in the United Kingdom on 16 April 2020
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Amazon Customer
5.0 out of 5 stars A must as a first book in Machine Learning
Reviewed in the United Kingdom on 25 July 2020
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Eric Petro
5.0 out of 5 stars Fast shipping & good textbook
Reviewed in Canada on 27 January 2019
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