Machine Learning: An Algorithmic Perspective

by Stephen Marsland

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.

Genres: Computer Science, Artificial Intelligence, Mathematics, Education, Technology, Nonfiction, Programming

458 pages, Hardcover

First published January 1, 2009

Reviews

[Simon Meißner]

Dieses Buch war Basis einer einführenden Veranstaltung an meiner Uni.

Das Buch vermittelt die Grundlagen von künstlichen neuronalen Netzen und maschinellen Lernen.
Dabei werden vor allem das single-, und multilayered Perceptron betrachtet. Zu Beginn gibt es ein paar einführende Kapitel, die einen guten Einstieg in das Thema verschaffen.

Leider wird das Buch nach kurzer Zeit relativ komplex, behandelt ausführlich mathematische Zusammenhänge und geht über das Grundlagenwissen weit hinaus. Was durchaus nicht schlecht ist aber für meinen individuellen Zeit- und Standpunkt zu tiefgreifend ist/war.

Mir hat sehr gefallen, dass der Autor begleitend zu dem Buch eine Website betreibt, die Testcode in python enthält, um die Theorie direkt in der Praxis umzusetzen. Außerdem werden am Ende jedes Kapitels, Fragen/Aufgaben gestellt um das Gelernte zu überprüfen.

[Kai Wolf]

Wonderful introduction to Machine Learning basics using Python (and numpy). Doesn't touch Deep Learning concepts as this book came out before the current AI hype really started to kick off.

[YOUSSEF AIDANI]

Even though it's so wordy the explanation are unclear and sometimes missing. Some concepts we superficially explained. Chapters are missing summaries. There are also frequent errors in formulas example the variance-bias error decomposition equation has clear problems in it in page 36

[zedoul]

So so, and Python codes was nice.

[Adri Molina]

A great book for starting digging into neural networks. The explanations are quite clear and straightforwardly written but kind of outdated as well.
I'd recommend it as a first step into machine learning algorithms before taking some hands-on approaches.

[Jessada Karnjana]

เป็นอีกเล่มหนึ่ง (ฉบับ second edition) ที่ใช้ประกอบการสอน ML ซึ่งในมุมของ algorithm ถือว่าชัดเจนดีทีเดียว มีข้ามรายละเอียด math บ้าง และหาเติมเต็มได้ด้วยเล่มอื่น ๆ สำหรับ algorithmic perspective ดีงาม

[Nick]

Some of the many text errors are kind of unforgivable.

[Rick Sam]

Look at this,



Would you read this, Giant Blog of Text?

[Brett Dargan]

I read this while I was reading Data Mining (weka one). Explanations in here are terse and in python, which helped me skip over some of the wordy explanations in Data Mining book.

[Alon Gutman]

Nice, but too mathematical, and go too deep on unimportant stuff on the one hand, and is missing some ML fundamentals on the other hand.

[Rishabh]

variable description missing in equations, everything else is lucid and very clear