MATLAB Machine Learning Recipes : A Problem-Solution Approach

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. 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 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. 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 ; Understaand 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 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