MATLAB Machine Learning Recipes | Taschenbuch

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Cover: 9781484239155 | MATLAB Machine Learning Recipes | A Problem-Solution Approach | Buch

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Sprache: Englisch

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Beschreibung

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.

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.

Inhaltsverzeichnis

1 Overview

2 Data Representation

3 MATLAB Graphics

4 Kalman Filters

5 Adaptive Control

6 Fuzzy Logic

7 Data Classification with Decision Trees

8 Simple Neural Nets

9 Classification with Neural Nets

10 Neural Nets with Deep Learning

11 Neural Aircraft Control

12 Multiple Hypothesis Testing

13 Autonomous Driving with MHT

14 Case-Based Expert Systems

Appendix A: A Brief History of Autonomous Learning

Appendix B: Software for Machine Learning

Details

Fachbereich:	Programmiersprachen
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Seiten:	347
ISBN-13:	9781484239155
ISBN-10:	1484239156
Sprache:	Englisch
Herstellernummer:	978-1-4842-3915-5
Autor:	Paluszek, Michael Thomas, Stephanie
Auflage:	2. Aufl.
Hersteller:	Apress Springer, Berlin
Abbildungen:	XIX, 347 p. 157 illus., 116 illus. in color.
Maße:	21 x 178 x 255 mm
Von/Mit:	Michael Paluszek (u. a.)
Erscheinungsdatum:	01.02.2019
Gewicht:	0,703 kg

preigu-id: 114096344