Overview
- Utilizes real world examples in MATLAB for major applications of machine learning in big data
- Comes with complete working MATLAB source code
- Shows how to use MATLAB graphics and visualization tools for machine learning
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.
What you'll learn:
- How to write code for machine learning, adaptive control and estimation using MATLAB
- How these three areas complement each other
- How these three areas are needed for robust machine learning applications
- How to use MATLAB graphics and visualization tools for machine learning
- How to code real world examples in MATLAB for major applications of machine learning in big data
Who is this book for:
The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.
Similar content being viewed by others
Keywords
Table of contents (14 chapters)
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 Recipes
Book Subtitle: A Problem-Solution Approach
Authors: Michael Paluszek, Stephanie Thomas
DOI: https://doi.org/10.1007/978-1-4842-3916-2
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Michael Paluszek and Stephanie Thomas 2019
eBook ISBN: 978-1-4842-3916-2Published: 31 January 2019
Edition Number: 2
Number of Pages: XIX, 347
Number of Illustrations: 41 b/w illustrations, 116 illustrations in colour
Topics: Artificial Intelligence, Big Data