Download PDF An Introduction to Machine Learning
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An Introduction to Machine Learning
Download PDF An Introduction to Machine Learning
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Review
“The presentation is mainly empirical, but precise and pedagogical, as each concept introduced is followed by a set of questions which allows the reader to check immediately whether they understand the topic. Each chapter ends with a historical summary and a series of computer assignments. … this book could serve as textbook for an undergraduate introductory course on machine learning … .†(Gilles Teyssière, Mathematical Reviews, April, 2017) “This book describes ongoing human-computer interaction (HCI) research and practical applications. … These techniques can be very useful in AR/VR development projects, and some of these chapters can be used as examples and guides for future research.†(Miguel A. Garcia-Ruiz, Computing Reviews, January, 2019)
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From the Back Cover
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,†how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
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Product details
Hardcover: 348 pages
Publisher: Springer; 2nd ed. 2017 edition (September 2, 2017)
Language: English
ISBN-10: 9783319639123
ISBN-13: 978-3319639123
ASIN: 3319639129
Product Dimensions:
6.1 x 0.8 x 9.2 inches
Shipping Weight: 1.5 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
2 customer reviews
Amazon Best Sellers Rank:
#585,688 in Books (See Top 100 in Books)
Excellent book!I was searching for books in a different subject and stumbled upon this gem. I bought the book I was initially looking for and decided to buy this one as well from the curiosity of learning about the field of "Machine Learning", since it has become so mainstream. I told myself I'll read it after i'm done with the other one. When I got the books I decided to glance at it out of curiosity. It's been over a month now since I got the books and I haven't touched the one I originally intended to read.Summary:Audience: Undergraduate Software Engineer or Similar (some knowledge of discrete math is required). An engineer with time to understand and implement the algorithms.Ease of read: Though this is subjective, so far I haven't found myself looking up words in a thesaurus. It written in simple english with the occasional mix of formalized mathematical definitions when necessary.Programming language: This books is language agnostic. Algorithms are presented with theory and "math style" pseudo code.Downloadable source code: none.Overall I highly recommend this book! Read the rest for more details:The book is very well written, in my opinion. It hooks you from the start with easy to understand writing and interesting introductions to the different subjects. It's also very well organized. From what I've read so far the current order of the chapters seem like the best way to read this book. Each section of the chapters has questions that will help you gauge how well you grasped the subject presented and each chapter reinforces your knowledge with exercises and computer assignments.My favorite part are the computer assignments as they challenge the reader's acquired knowledge. The author of the book specifically mentions that the book doesn't come with downloadable source code intentionally. The idea is to make the reader truly understand the subject by implementing the algorithms themselves. At first, I thought that was a lazy approach from the author. However, after a couple of chapters in, I've come to experience the insight and confidence gained from implementing the assignments yourself. Risky move that will definitely turn off many potential readers, but likely to gain the respect of those who are willing to put in the time. That means, however, if you are looking to get jump started on implementing machine learning algorithms, this is probably not the first book you should look for. Nevertheless, it should still be in your library as it can serve as supplemental or reference material.Another thing I liked about this book is that it's language agnostic. This book is completely theoretical and the algorithms can be implemented by the language of your choice. The authors targeted audience is clearly software engineers or similar, hence, all algorithms come with pseudo code and examples to help understand how to implement them. The book does require some level of math: undergraduate discrete math is probably the main one that comes to mind.Lastly, I would highly recommend this book to any software engineer who has the time to invest into implementing the algorithms or someone that has an undergraduate software engineer like math level and wants to learn about machine learning.
This is a good book to learn the technical aspects of Machine Learning. As an aside, artificial intelligence, robotics and machine learning are going to render real human more and more irrelevant and dispensable. Robots will become the value creators and human will become more and more a bunch of useless parasites without meaningful works and not really knowing why they exit on earth at all.
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An Introduction to Machine Learning PDF
An Introduction to Machine Learning PDF
An Introduction to Machine Learning PDF
An Introduction to Machine Learning PDF
