MATLAB Machine Learning Recipes

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution.

This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.

What You Will Learn

Write code for machine learning, adaptive control, and estimation using MATLAB Use MATLAB graphics and visualization tools for machine learning Become familiar with neural nets Build expert systems Understand adaptive control Gain knowledge of Kalman Filters

Who This Book Is For:
Software engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists.

1. Front Matter

2. An Overview of Machine Learning

3. Data for Machine Learning in MATLAB

4. MATLAB Graphics

5. Kalman Filters

6. Adaptive Control

7. Fuzzy Logic

8. Neural Aircraft Control

9. Introduction to Neural Nets

10. Classification of Numbers Using Neural Networks

11. Data Classification with Decision Trees

12. Pattern Recognition with Deep Learning

13. Multiple Hypothesis Testing

14. Autonomous Driving with MHT

15. Spacecraft Attitude Determination

16. Case-Based Expert Systems

17. Back Matter