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Stephen Marsland

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Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Patrtern Recognition) ペーパーバック – 2009/4/8

英語版 Stephen Marsland (著)

3.9 31個の評価

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Traditional books on machine learning can be divided into two groups ― those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.

Theory Backed up by Practical Examples

The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.

Highlights a Range of Disciplines and Applications

Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.

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本の長さ

406ページ
言語

英語
出版社

Chapman and Hall/CRC
発売日

2009/4/8
寸法

17.15 x 2.54 x 24.77 cm
ISBN-10

1420067184
ISBN-13

978-1420067187
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… liberally illustrated with many programming examples, using Python. It includes a basic primer on Python and has an accompanying website.
It has excellent breadth, and is comprehensive in terms of the topics it covers, both in terms of methods and in terms of concepts and theory. …
I think the author has succeeded in his aim: the book provides an accessible introduction to machine learning. It would be excellent as a first exposure to the subject, and would put the various ideas in context …
This book also includes the first occurrence I have seen in print of a reference to a zettabyte of data (10²¹ bytes) ― a reference to "all the world’s computers" being estimated to contain almost a zettabyte by 2010.
―David J. Hand, International Statistical Review (2010), 78

If you are interested in learning enough AI to understand the sort of new techniques being introduced into Web 2 applications, then this is a good place to start. … it covers the subject matter of many an introductory course on AI and it has references to the source material and further reading but it is written in a fairly casual style. Overall it works and much of the mathematics is explained in ways that make it fairly clear what is going on … . This is a suitable introduction to AI if you are studying the subject on your own and it would make a good course text for an introduction and overview of AI.
―I-Programmer, November 2009

著者について

Massey University, Palmerston North, New Zealand

登録情報

出版社 ‏ : ‎ Chapman and Hall/CRC; 第1版 (2009/4/8)
発売日 ‏ : ‎ 2009/4/8
言語 ‏ : ‎ 英語
ペーパーバック ‏ : ‎ 406ページ
ISBN-10 ‏ : ‎ 1420067184
ISBN-13 ‏ : ‎ 978-1420067187
寸法 ‏ : ‎ 17.15 x 2.54 x 24.77 cm

Amazon 売れ筋ランキング: - 441,053位洋書 (洋書の売れ筋ランキングを見る)
- - 684位Computer Neural Networks
- - 697位Data Mining
- - 1,261位Programming Algorithms

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Stephen Marsland

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アマゾンカスタマー

Best for the beginner and who want fundamental knowledge

2010年12月3日に日本でレビュー済み

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This is a machine learning book designed for people ranging from beginner to intermediate class.
The book it self is written with plain English, so easy to understand without much background of machine learning.
I strongly recommend this book as Teach yourself book.

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keiichi_ito

平易な解説と簡潔なプログラムが理解に役立つ

2014年7月9日に日本でレビュー済み

分かりやすい解説で入門に最適。著者のホームページから本書で掲載されている手法のPythonで書かれたプログラムも入手できる。ダウンロードしたプログラムにはけっこうバグがあったが、それらを直したり応用したりする過程がとても勉強になった。ニューラルネットワーク、RBF、SVM、CART、アンサンブル学習、強化学習、遺伝的アルゴリズム、ベイズネットワーク等、広く浅くお試し出来るところが良い。

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Daniele C.

Una rara gemma!

2013年3月28日にイタリアでレビュー済み

Amazonで購入

Questo libro ha il grande merito di esporre le principali tecniche di machine learning, tra cui reti neurali, support vector machine, alberi di decisione e algoritmi genetici, con estrema chiarezza e semplicità, per un pubblico estremamente ampio.
Viene fornita e dettagliatamente spiegata, un'elegante ed efficace implementazione Python (basata sulle librerie Numpy) su esempi pratici, per molte delle tecniche trattate (e sì non è solo teoria!)
Non aspettatevi una trattazione accurata degli ultimissimi algoritmi come le Deep Belief Network, né dimostrazione matematiche particolarmente complicate. Rimane un testo introduttivo al machine learning ma la chiarezza dei concetti, l'accessibilità dei contenuti e l'eleganza dei programmi allegati ne fanno veramente una rara gemma, adatta per tutti coloro che abbiamo un background matematico almeno da scuole superiori e conoscano discretamente l'Inglese.

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M. Hecht

Super für Python Programmierer

2013年1月30日にドイツでレビュー済み

Amazonで購入

Ich beschäftige mich schon seit Jahren mit dem Thema Machine Learning und hatte das Buch schon eine Weile. Da ich nun viel mit Phyton programmiere, fiel mir das Buch wieder in die Hand. JA, um zu vertehen, was Machine Learning und Data Mining eigentlich ist, ist dieses Buch sicher weniger geeignet. Allerdings lassen sich alle Codes direkt in Python nutzen und deren prinzipielle Funktionalität kann man im Buch mit Hintergrundinformationen nachlesen. Es gibt zwar jede Menge Machine Learning Bibliotheken im Web, aber deren Anwendung ist oftmals überaus fummelig und es gibt meist jede Menge Abhängigkeiten von anderen Bibliotheken oder seltsame Datenformate, etc.. Hier gibt es pures Python für eigenen Anwendungen, daher volle Punktzahl.

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John

A great book for students

2010年2月6日にアメリカ合衆国でレビュー済み

Amazonで購入

This is an good book on machine learning for students at the advanced
undergraduate or Masters level, or for self study, particularly if
some of the background math (eigenvectors, probability theory, etc)
is not already second nature.

Although I am now familiar with much of the math in this area and consider
myself to have intermediate knowledge of machine learning, I can still recall
my first attempts to learn some mathematical topics. At that time my approach
was to implement the ideas as computer programs and plot the results. This
book takes exactly that approach, with each topic being presented both
mathematically and in Python code using the new Numpy and Scipy libraries.
Numpy resembles Matlab and is sufficiently high level that the book code
examples read like pseudocode.

(Another thing I recall when I was first learning was the mistaken
belief that books are free from mistakes. I've since learned to
expect that every first edition is going to have some, and doubly so
for books with math and code examples. However the fact that many of the examples
in this book produce plots is reassuring.)

As mentioned I have only intermediate knowledge of machine learning, and
have no experience with some techniques. I learned regression trees
and ensemble learning from this book -- and then implemented an ensemble
tree classifier that has been quite successful at our company.

Some other strong books are the two Bishop books (Neural Networks for Pattern
Recognition; Pattern Recognition and Machine Learning),
Friedman/Hastie/Tibshirani (Elements of Statistical Learning) and
Duda/Hart/Stork (Pattern Classification). Of these, I think the first Bishop
book is the only other text suitable for a beginner, but it doesn't have the
explanation-by-programming approach and is also now a bit dated (Marsland
includes modern topics such as manifold learning, ensemble learning, and a bit
of graphical models). Friedman et al. is a good collection of algorithms,
including ones that are not presented in Marsland; it is a bit dry however.
The new Bishop is probably the deepest and best current text, but it is
probably most suited for PhD students. Duda et al would be a good book at a
Masters level though its coverage of modern techniques is more limited. Of
course these are just my impressions. Machine learning is a broad subject and
anyone using these algorithms will eventually want to refer to several of these books.
For example, the first Bishop covers the normalized flavor of radial basis
functions (a favorite technique for me), and each of the mentioned books has
their own strengths.

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Valerio Maggio

Ottimo manuale pronto all'uso

2013年6月30日にイタリアでレビュー済み

Amazonで購入

Credo che sia un ottimo manuale da utilizzare per consultazione rapida ( e chiara ).

Ogni problema di Machine Learning è trattato senza eccessivi preamboli ed ogni algoritmo è corredato dal proprio pseudocodice, successivamente declinato in codice Python ( Numpy, Scipy) pronto all'uso.

Sebbene fornisca il background per ciascun argomento ( non richiedendo pertanto particolare conoscenza pregressa per la sua comprensione ), ci sono caso in cui alcuni concetti sono dati "per scontato" o, comunque, non contengono eccessivi dettagli specifici. Inoltre, il taglio è molto orientato verso Neural Networks e AI ( piuttosto che pattern matching e/o statistical Models ) come altri testi ( vedi Bishop o Mitchell )

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doug

Different than other 'textbooks' on ML

2010年11月8日にアメリカ合衆国でレビュー済み

Amazonで購入

I first saw this book on a colleague's bookshelf; i picked it up and briefly looked through it. The simple diagrams and the relative lack of equations (compared to e.g., Bishop) might suggest to you that it's a 'beginner' text--and by that i mean that the textbook is only an introduction to ML and doesn't teach you enough so that you can begin writing ML code to solve real classification/regression problems. That's what i though at first, and i was wrong. This is an introductory text, but only in the sense that it's accessible to more or less anyone, but this book's explanation/theory and the practical examples (in python) are brilliantly integrated--the explanation (often summarizing two or three pages of terse equations found in other textbooks, in a single paragraph) helped me grok the code, and the code reinforced the theory behind the algorithm.

I don't think there's another ML book like this--it's aimed right at the blind spot framed by applied math reference-type books such as Bishop on one end, and books like 'Programming Collective Intelligence' which are dense with working ML code, but light on theory.

I also like this book because the code is written in NumPy, rather than in the Python standard library code. NumPy is what you would use 'in the real world' to code an ML algorithm, and if you understand the matrix-driven syntax, then the code is far more concise (e.g., no triply nested recursive loops) than the same algorithms coded using just the Python standard library.

In sum, an excellent book.

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Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Patrtern Recognition) ペーパーバック – 2009/4/8

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Stephen Marsland

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