MATLAB Machine Learning Recipes : A Problem-Solution Approach / by Michael Paluszek, Stephanie Thomas.

Format:

Book

Author/Creator:

Paluszek, Michael, Author.

Thomas, Stephanie, Author.

Language:

English

Subjects (All):

MATLAB.

Artificial intelligence.

Big data.

Artificial Intelligence.

Big Data.

Local Subjects:

Artificial Intelligence.

Big Data.

Physical Description:

1 online resource (358 pages)

Edition:

2nd ed. 2019.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2019.

System Details:

text file

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. You will: Learn to write code for machine learning, adaptive control and estimation using MATLAB See how these three areas complement each other Understand why these three areas are needed for robust machine learning applications Use MATLAB graphics and visualization tools for machine learning Code real world examples in MATLAB for major applications of machine learning in big data .

Contents:

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.

Notes:

Includes bibliographical references.

ISBN:

9781523150373

1523150378

9781484239162

1484239164

OCLC:

1104211878