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A First Course in Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition), by Simon Rogers, Mark Girolami
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A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail.
Referenced throughout the text and available on a supporting website (http://bit.ly/firstcourseml), an extensive collection of MATLAB®/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems.
Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.
- Sales Rank: #1127148 in Books
- Brand: Brand: Chapman and Hall/CRC
- Published on: 2011-10-25
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 6.25" w x .75" l, .88 pounds
- Binding: Hardcover
- 305 pages
- Used Book in Good Condition
Review
"This book offers an introduction to machine learning for students with rather limited background in mathematics and statistics. ... The book is well written and focusses on explaining themain concepts at a very basic level, keeping in mind the limited mathematical background of the intended audience. There are also useful references for further reading at the end of each chapter, and MATLAB code implementing the methods is available online along with the data sets. The code also seems to work well with free alternatives to MATLAB like Octave and FreeMat."
―Thoralf Mildenberger, IDP Institute of Data Analysis and Process Design, Zurich University of Applied Sciences, writing in Stat Papers (2015) 56:271
"… the authors do well to keep complicated mathematical notation of the kind sometimes found in statistical texts to a minimum. The concepts are introduced in quite a simple way so as to be intelligible to a reader with no statistical background. … this introductory text will be useful to computer scientists who need some basic introduction to statistical methods to apply in their respective problems …"
―Arindam Sengupta, International Statistical Review, 2014
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 is a chair of statistics and an honorary professor of computer science at University College London, where he is also the director of the Centre for Computational Statistics and Machine Learning. An EPSRC Advanced Research Fellow, an IET Fellow, and a Fellow of the Royal Society of Edinburgh, Dr. Girolami has made major contributions to the field, including his generalisation of independent component analysis, his work on inference in systems biology, and his innovations in statistical methodology.
Most helpful customer reviews
8 of 9 people found the following review helpful.
Not very good
By RW
For a book so namely titled it lacks a lot of detailed information. I am very well versed in probability and statistics and still had a hard time following. The author uses notations out of the blue which leaves you feeling like "what just happened here". I bought this book with the hopes of gaining a better understanding but it leaves a lot to be desired. The book's title is very misleading and should be renamed.
2 of 2 people found the following review helpful.
Machine Learning begins here
By sameer
A very good book to finally get my head around Bayesian Learning. As mentioned in the preface, the authors focus on depth rather than breadth. They work out the math in detail for the examples provided. Being machine learning, some background is essential which includes math and programming. Almost all books in this subject require these pre-reqs (at the level expected for a grad level course or senior undergrad).
Strengths:
- Very good order of introducing topics - loss functions, probabilistic view, Bayesian view, etc.
- Detailed working of a few good examples with code also provided for further exploration.
- Introduces linear algebra in parts. In some of the other books, they dive into Linear algebra - positive semi-definite, trace, etc. within no time. Here the authors introduce these concepts gradually, and include sections to highlight why this matters. Matrix notation makes the notations extremely simple, however, if not understood properly, we just see a bunch of symbols like sigma, etc all over the text.
- In addition, another strength I feel is how self contained the book is in terms of explanation. While reading Barber or Murphy (both excellent texts in themselves), I used to get lost pretty quickly and had to look up for additional help on the web before moving on. For this book, I have been able to read chapter after chapter, without having to rush to google every few mins.
With regards to other reviews: The notations were not confusing (atleast to me), the programming with matlab is ok (most of the languages, including python, Lua share similar syntax for matrix manipulations).
I would also mention that I am a grad student and have already taken a couple of machine learning courses. So, it might have been a little easier to go through a "first course" this time around. However, I really found this to be a great text for revision and in general understand the key concepts in this subject area. I also feel that the lack of breadth is not a real concern. As mentioned in another review, if one is able to understand the concepts in this book, they can be carried on to help improve understanding more techniques.
It would have been great to have this as a college text.
1 of 1 people found the following review helpful.
For a fast overview of most useful concepts of machine learning
By guest
This book is built on a course given at the university. Therefore, do not think to get a thorough and quite outstanding overview of the field like the Barber's or the Bishop's that are excellent. However, at the contrary of other reviews, I find it useful as a refresher. It is concise and sometimes force you to elaborate by yourself to check the formulations, structures well the basics. Not bad at all.
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