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.

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.

MATLAB Machine Learning Recipes: A Problem-Solution Approach