E-Book, Englisch, 335 Seiten, eBook
ISBN: 978-1-4842-2250-8
Verlag: APRESS
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
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 providescomplete 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.
What you'll learn:
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Weitere Infos & Material
1 Overview of Machine Learning.- 2 The History of Machine Learning.- 3 Software for machine learning.- 4 Representation of data for Machine Learning in MATLAB.- 5 MATLAB Graphics.- 6 Machine Learning Examples in MATLAB.- 7 Face Recognition with Deep Learning.- 8 Data Classification.- 9 Classification of Numbers Using Neural Networks.- 10 Kalman Filters.- 11 Adaptive Control.- 12 Autonomous Driving.- Bibliography.