MATLAB machine learning recipes : A problem-solution approach

By:

Paluszek, Michael

Contributor(s):

Thomas, Stephanie

Language: ENG Publication details: New York Apress 2019Edition: 2ndDescription: xix, 347p. | Binding - Paperback | 24*16.5 cmISBN:

9781484252413

Subject(s):

EXTC Engineering

DDC classification:

DDC23 PAL/THO 006.31

Online resources:

Source Code

Summary: Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. 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.

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Item type	Current library	Collection	Call number	Status	Barcode
Books	School of Engineering & Technology Reference Section	Reference	006.31 PAL/THO (Browse shelf(Opens below))	Available	E15178

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Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. 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.

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