Overview
- A first to market practical guide for using MATLAB to write machine learning software
- Numerous worked examples spanning the field of machine learning and big data
- Comes with complete working MATLAB source code
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Table of contents (12 chapters)
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Introduction to Machine Learning
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MATLAB Recipes for Machine Learning
Keywords
About this book
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.
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Authors and Affiliations
About the authors
Stephanie Thomas is the co-author of MATLAB Recipes, published by Apress. She received her bachelor's and master's degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 1999 and 2001. Ms. Thomas was introduced to PSS' Spacecraft Control Toolbox for MATLAB during a summer internship in 1996 and has been using MATLAB for aerospace analysis ever since. She built a simulation of a lunar transfer vehicle in C++, LunarPilot, during the same internship. In her nearly 20 years of MATLAB experience, she has developed many software tools including the Solar Sail Module for the Spacecraft Control Toolbox; a proximity satellite operations toolbox for the Air Force; collision monitoring Simulink blocks for the Prisma satellite mission; and launch vehicle analysis tools in MATLAB and Java, to name a few. She has developed novel methods for space situation assessment such as a numeric approach to assessing the general rendezvous problem between any two satellites implemented in both MATLAB and C++. Ms. Thomas has contributed to PSS' Attitude and Orbit Control textbook, featuring examples using the Spacecraft Control Toolbox, and written many software User's Guides. She has conducted SCT training for engineers from diverse locales such as Australia, Canada, Brazil, and Thailand and has performed MATLAB consulting for NASA, the Air Force, and the European Space Agency.
Bibliographic Information
Book Title: MATLAB Machine Learning
Authors: Michael Paluszek, Stephanie Thomas
DOI: https://doi.org/10.1007/978-1-4842-2250-8
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Michael Paluszek, Stephanie Thomas 2017
eBook ISBN: 978-1-4842-2250-8Published: 28 December 2016
Edition Number: 1
Number of Pages: XIX, 326
Number of Illustrations: 66 b/w illustrations, 74 illustrations in colour
Topics: Artificial Intelligence, Programming Languages, Compilers, Interpreters, Programming Techniques