Apress - MATLAB Machine Learning (2017 EN)

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    Author: Michael Paluszek, Stephanie Thomas
    Full Title: MATLAB Machine Learning
    Publisher: Apress; 1st ed. edition (December 29, 2016)
    Year: 2017
    ISBN-13: 9781484222508 (978-1-4842-2250-8), 9781484222492 (978-1-4842-2249-2)
    ISBN-10: 1484222504, 1484222490
    Pages: 326
    Language: English
    Genre: Programming Techniques
    File type: EPUB (True), PDF (True)
    Quality: 10/10
    Price: 46.79 €

    This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning.

    The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results.

    Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology.

    The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book.

    ✓ An overview of the field of machine learning
    ✓ Commercial and open source packages in MATLAB
    ✓ How to use MATLAB for programming and building machine learning applications
    ✓ MATLAB graphics for machine learning
    ✓ Practical real world examples in MATLAB for major applications of machine learning in big data

    Who is this book for:
    The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.


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