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Machine Learning: The Art and Science of Algorithms that Make Sense of Data (Englisch) Taschenbuch – 20. September 2012

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  • Taschenbuch: 409 Seiten
  • Verlag: Cambridge University Press; Auflage: New. (20. September 2012)
  • Sprache: Englisch
  • ISBN-10: 1107422221
  • ISBN-13: 978-1107422223
  • Größe und/oder Gewicht: 18,9 x 1,8 x 24,6 cm
  • Durchschnittliche Kundenbewertung: 3.5 von 5 Sternen  Alle Rezensionen anzeigen (2 Kundenrezensionen)
  • Amazon Bestseller-Rang: Nr. 65.321 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
  • Komplettes Inhaltsverzeichnis ansehen

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"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms." < /br>Fernando Berzal, Computing Reviews

Über das Produkt

Machine Learning brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, the book explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.

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0 von 1 Kunden fanden die folgende Rezension hilfreich Von Mario Boley am 8. Juni 2014
Format: Taschenbuch Verifizierter Kauf
This book fills a gap in machine learning literature: all important topics are directly accessible, while at the same time all content is presented mathematically rigorously from a unified perspective.
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1 von 5 Kunden fanden die folgende Rezension hilfreich Von Beebear am 10. Dezember 2013
Format: Taschenbuch Verifizierter Kauf
It's not such a satisfactory read as some more expensive books. However, it offers a couple of insights that are not found in more standard recommendations. There are many unusual approaches such as referencing ROC curves a lot. Also the referenced material and reading suggestions are quite good. The layout is pretty neat too.
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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)

Amazon.com: 17 Rezensionen
76 von 81 Kunden fanden die folgende Rezension hilfreich
Excellent introductory text with significant depth for professionals: future classic 24. Januar 2013
Von Scott C. Locklin - Veröffentlicht auf Amazon.com
Format: Taschenbuch Vine Kundenrezension eines kostenfreien Produkts ( Was ist das? )
I'm probably to be considered a "very advanced amateur" or "informal professional" in machine learning techniques. I never studied them in school, but I presently make a living in part by coding up new ones and coming up with ML solutions for commercial problems. Confronting the field for the first time, I wondered how people learned the stuff. Most of the introductory texts just covered neural nets ... and very very badly. Neural nets are still useful, and probably the most mature of machine learning techniques, but throwing them at a beginner without context is a recipe for confusion and dismay.

This text, by contrast, barely mentions them, and puts them in their proper context for the beginner. The right way to think about machine learning is starting with *very* basic statistical techniques and probability theory, and building up from there into simple classification and scoring systems, and then on to the rest of the field. The author of this text does it the right way.

One of the difficulties of didactic texts in the subject is ... machine learning is a very diverse field. All kinds of gizmos are helpful, and there isn't an obvious taxonomy, as there is in, say, linear time series models. The author takes a very high level view; breaking the field down into geometric, probabilistic and "logical" models. I believe this to be original, and a very powerful way of looking at things for the beginner.

The progression is well thought out, and each chapter comes with a useful summary and references (one of which has already proved helpful to me) for further reading. The summaries are not mere recitations; they contain useful facts about the strengths and weaknesses of different varieties of models covered, as well as deep insights that are buried beneath useless verbiage and equations in other texts. There are things covered here which are covered in precious few advanced texts: for example, inductive concept learning seemed to fall off the map after the "AI winter," at least as far as most texts were concerned. The book also manages to take the reader to fairly complex topics: boosting, bagging, lasso, meta-learning and random forests. The last chapter has useful guidance on out of scope topics (reinforcement learning, sequence prediction, etc).

The text is well written and has attractive and helpful graphs and figures. Better yet, the layout, color scheme and typesetting is the best I have ever seen in a book of this kind. This seems like a small thing, but far too many color texts end up looking like old school wired magazines: they hurt the eyes with useless graphics and insane colors. Just because you have a broad palette doesn't mean you should use all those colors. The typesetter gets this one just right. I commend the author on his eye for beauty here.

Downsides: as a unifying didactic text for beginners (or review for non-beginners), it is near perfect, but it does lack problem sets. For me, this is a feature, rather than a bug, but people teaching from this book might find it tedious to come up with problems which go with the text. Perhaps sticking some online would be appropriate at some point.
35 von 38 Kunden fanden die folgende Rezension hilfreich
An gentle, very well written, introduction to Machine Learning 2. April 2013
Von Ian Kaplan - Veröffentlicht auf Amazon.com
Format: Taschenbuch Vine Kundenrezension eines kostenfreien Produkts ( Was ist das? )
In my Advanced Statistics class one of the text books was The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. I found some of the chapters in this book heavy going and I had to read them several times and ask the professor lots of questions before I understood the material.

In contrast, Machine Learning by Peter Flach is a very well written, very gentle introduction to machine learning algorithms. Prof. Flach writes that he spent four years writing this book and it shows in the care with which the material is presented.

The mathematics used is algebra, exponents, summations, products and a bit of linear algebra. There are only a few places where derivatives are used (as it turns out, basic linear algebra can be used to describe many machine learning algorithms). The level of the Machine Learning makes it appropriate for an undergraduate Machine Learning course.

Machine Learning covers most of the core algorithms in machine learning. Of necessity what is provided is an overview of topics like linear regression and linear classifiers like Support Vector Machines. These are topics that are covered in depth in book like Applied Regression Analysis and An Introduction to Support Vector Machines and Other Kernel-based Learning Methods.

Reading this book without taking a class in machine learning can be difficult because there are no exercises with solutions or projects with data. Machine learning is a practical art. It is very difficult to read a ~360 page book and absorb all of the material without the reinforcement of applying the machine learning techniques. As I read the book I constantly wished that the author had included exercises with the associated data on his web site for the major approaches. This would also make the book easier to use for a machine learning course, since the professor would not have to develop all of the homework exercises.
11 von 12 Kunden fanden die folgende Rezension hilfreich
A Definitive Primer on Machine Learning 14. Januar 2014
Von Adnan Masood - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Over a decade ago, Peter Flach of Bristol University wrote a paper on the topic of "On the state of the art in machine learning: A personal review" in which he reviewed several, then recent books, related to developments in machine learning. This included Pat Langley’s Elements of Machine Learning (Morgan Kaufmann), Tom Mitchell’s Machine Learning (McGraw-Hill), and Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations by Ian Witten and Eibe Frank (Morgan Kaufman) among many others. Dr. Flach mentioned Michael Berry and Gordon Linoff’s Data Mining Techniques for Marketing, Sales, and Customer Support (John Wiley) for it's excellent writing style citing the paragraph below and commending "I wish that all computer science textbooks were written like this."

“People often find it hard to understand why the training set and test set are “tainted” once they have been used to build a model. An analogy may help: Imagine yourself back in the 5th grade. The class is taking a spelling test. Suppose that, at the end of the test period, the teacher asks you to estimate your own grade on the quiz by marking the words you got wrong. You will give yourself a very good grade, but your spelling will not improve. If, at the beginning of the period, you thought there should be an ‘e’ at the end of “tomato”, nothing will have happened to change your mind when you grade your paper. No new data has entered the system. You need a test set! Now, imagine that at the end of the test the teacher allows you to look at the papersof several neighbors before grading your own. If they all agree that “tomato” has no final ‘e’, you may decide to mark your own answer wrong. If the teacher gives the same quiz tomorrow, you will do better. But how much better? If you use the papers of the very same neighbors to evaluate your performance tomorrow, you may still be fooling yourself. If they all agree that “potatoes” has no more need of an ‘e’ then “tomato”, and you have changed your own guess to agree with theirs, then you will overestimate your actual grade on the second quiz as well. That is why the evaluation set should be different from the test set.” [3, pp. 76–77] Machine-Learning-9781107096394

That is why when I recently came across "Machine Learning The Art and Science of Algorithms that Make Sense of Data", I decided to check it out and wasn't disappointed. Dr. Flach is the Professor of Artificial Intelligence at the University of Bristol and in this "future classic", he left no stone unturned when it comes to clarity and explainability. The book starts with a machine learning sampler, introduces the ingredients of machine learning fast progressing to Binary classification and Beyond. Written as a textbook, riddled with examples, foot-notes and figures, this text elaborates concept learning, tree models, rule models, linear models, distance-based models, probabilistic models to features and ensembles concluding with Machine learning experiments. I really enjoyed the "Important points to remember" section of the book as a quick refresher on machine-learning-commandments.

The concept learning section seems to have been influenced by author's own research interest and is not discussed in as much details in contemporary machine learning texts. I also found frequent summarization of concepts to be quite helpful. Contrary to it's subtitle and compared to it's counterparts, the book however is light on algorithms and code, possibly on purpose. While it explains the concepts with examples, number of formal algorithms are kept to a minimum. This may aid in clarity and help avoiding recipe-book-syndrome while making it potentially inaccessible to practitioners. Great at basics, the text also falls short on elaboration of intermediate to advance topics such as LDA, kernel methods, PCA, RKHS, and convex optimization. For instance, in chapter 10 "Matrix transformations and decompositions" could have been made an appendix while expanding upon meaningful topics like LSA and use cases of sparse matrix (pg 327). It is definitely not the book's fault; but rather of this reader expecting too much from an introductory text just because author explains everything so well!

As a text book on Machine learning as the Art and Science of Algorithms, Peter Flach definitely delivers on the promise of clarity, with well chosen illustrations and example based approach. A highly recommended reading for all who would like to understand the principles behind machine learning techniques.

Materials can be downloaded from here which generously include excerpts with background material and literature references, full set of 540 lecture slides in PDF including all figures in the book with LaTeX beamer source of the above.
17 von 20 Kunden fanden die folgende Rezension hilfreich
A good introduction to data mining 3. Februar 2013
Von A. J Terry - Veröffentlicht auf Amazon.com
Format: Taschenbuch Vine Kundenrezension eines kostenfreien Produkts ( Was ist das? )
This book defines machine learning as programs that learn from experience, but it focuses mostly on data mining. There is a two-page nod to all the other kinds of machine learning, such as all the reinforcement learning work. I'd prefer data mining to be called data mining. However, this is a good first book on data mining.

Machine Learning first talks about the tasks and output of machine learning. Author Peter Flach surveys the various types of models: Tree models (e.g., decision trees), rule models, linear models (e.g., regression and SVM), distance-based models (e.g., k Means), and probabilistic models. He covers all the Usual Suspects, with no surprises. He ends with a discussion of features and a little coverage of ensemble methods. There aren't a ton of references, but the bibliography is reasonably long.

The methods are presented with the math in matrix algebra form, which I like, and with a high-level algorithm in pseudocode. The book is well illustrated. It would be nice if it contained more discussion of how to know when to believe the resulting model, how to judge quality.
5 von 5 Kunden fanden die folgende Rezension hilfreich
clear discussion of several models 3. Februar 2013
Von W Boudville - Veröffentlicht auf Amazon.com
Format: Taschenbuch Vine Kundenrezension eines kostenfreien Produkts ( Was ist das? )
Flach provides you with a very readable and concise book that covers several vast fields of computation. The subject is big data, to use a recent trendy label. He provides a clear narrative, where you can quickly understand the key underlying ideas. Amusingly, the first chapter has a long example about how to distinguish spam [unwanted bulk mail] from ham [regular mail]. A good pedagogic choice, for you learn about conditional probabilities and Bayes classification. It turns out that Bayesian approaches are common in the entire field of machine learning. So the first chapter correctly confronts this point and educates the reader.

I would also point you to Figure 1.7. It is a qualitative 2 dimensional map of the models treated in the text. Models sharing characteristics are close together in the map. You get a top level qualitative appreciation of their properties. Useful when making decisions which to apply.

The text also talks about a common problem when dealing with high dimensional data - the curse of dimensionality. As the number of dimensions increases, the data gets sparser. Ideally you want the data to be on a manifold - a surface of lower dimensionality unto which you can project the data prior to further analysis. Of course this just begs the question about how you can detect such a manifold. The book really doesn't say much further other than perhaps to look at a histogram of pairwise distances from a sampling.

The section on support vector machines is especially nicely written. It cuts through a lot of jargon that exists in this topic. The geometric visualisation of what you are trying to find - a hyperplane that separates the data into 2 qualitatively different sections - is the main idea to keep in mind.
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