MATLAB Machine Learning Recipes: A Problem-Solution Approach

✍ Scribed by Michael Paluszek, Stephanie Thomas

• Publisher: Apress
• Year: 2018
• Tongue: English
• Leaves: 358
• Edition: 2
• Category: Library

✦ Synopsis

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.  

✦ Table of Contents

Front Matter ....Pages I-XIX
An Overview of Machine Learning (Michael Paluszek, Stephanie Thomas)....Pages 1-18
Representation of Data for Machine Learning in MATLAB (Michael Paluszek, Stephanie Thomas)....Pages 19-43
MATLAB Graphics (Michael Paluszek, Stephanie Thomas)....Pages 45-71
Kalman Filters (Michael Paluszek, Stephanie Thomas)....Pages 73-108
Adaptive Control (Michael Paluszek, Stephanie Thomas)....Pages 109-133
Fuzzy Logic (Michael Paluszek, Stephanie Thomas)....Pages 135-146
Data Classification with Decision Trees (Michael Paluszek, Stephanie Thomas)....Pages 147-169
Introduction to Neural Nets (Michael Paluszek, Stephanie Thomas)....Pages 171-186
Classification of Numbers Using Neural Networks (Michael Paluszek, Stephanie Thomas)....Pages 187-207
Pattern Recognition with Deep Learning (Michael Paluszek, Stephanie Thomas)....Pages 209-230
Neural Aircraft Control (Michael Paluszek, Stephanie Thomas)....Pages 231-264
Multiple Hypothesis Testing (Michael Paluszek, Stephanie Thomas)....Pages 265-290
Autonomous Driving with Multiple Hypothesis Testing (Michael Paluszek, Stephanie Thomas)....Pages 291-310
Case-Based Expert Systems (Michael Paluszek, Stephanie Thomas)....Pages 311-316
Back Matter ....Pages 317-347