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.