MATLAB Machine Learning

Contents at a Glance

Contents

About the Authors

About the Technical Reviewer

Introduction

Part I Introduction to Machine Learning

Chapter 1:An Overview of Machine Learning

1.1 Introduction

1.2 Elements of Machine Learning

1.2.1 Data

1.2.2 Models

1.2.3 Training

1.2.3.1 Supervised Learning

1.2.3.2 Unsupervised Learning

1.2.3.3 Semisupervised Learning

1.2.3.4 Online Learning

1.3 The Learning Machine

1.4 Taxonomy of Machine Learning

1.5 Autonomous Learning Methods

1.5.1 Regression

1.5.2 Neural Nets

1.5.3 Support Vector Machines

1.5.4 Decision Trees

1.5.5 Expert System

References

Chapter 2:The History of Autonomous Learning

2.1 Introduction

2.2 Artificial Intelligence

2.3 Learning Control

2.4 Machine Learning

2.5 The Future

References

Chapter 3:Software for Machine Learning

3.1 Autonomous Learning Software

3.2 Commercial MATLAB Software

3.2.1 MathWorks Products

3.2.1.1 Statistics and Machine Learning Toolbox

3.2.1.2 Neural Network Toolbox

3.2.1.3 Computer Vision System Toolbox

3.2.1.4 System Identification Toolbox

3.2.2 Princeton Satellite Systems Products

3.2.2.1 Core Control Toolbox

3.2.2.2 Target Tracking

3.3 MATLAB Open-Source Resources

3.3.1 Deep Learn Toolbox

3.3.2 Deep Neural Network

3.3.3 MatConvNet

3.4 Products for Machine Learning

3.4.1 R

3.4.2 scikit-learn

3.4.3 LIBSVM

3.5 Products for Optimization

3.5.1 LOQO

3.5.2 SNOPT

3.5.3 GLPK

3.5.4 CVX

3.5.5 SeDuMi

3.5.6 YALMIP

References

Part II MATLAB Recipes for Machine Learning

Chapter 4:Representation of Data for Machine Learning in MATLAB

4.1 Introduction to MATLAB Data Types

4.1.1 Matrices

4.1.2 Cell Arrays

4.1.3 Data Structures

4.1.4 Numerics

4.1.5 Images

4.1.6 Datastore

4.1.7 Tall Arrays

4.1.8 Sparse Matrices

4.1.9 Tables and Categoricals

4.1.10 Large MAT-Files

4.2 Initializing a Data Structure Using Parameters

4.2.1 Problem

4.2.2 Solution

4.2.3 How It Works

4.3 Performing mapreduce on an Image Datastore

4.3.1 Problem

4.3.2 Solution

4.3.3 How It Works

4.4 Creating a Table from a File

Summary

Chapter 5MATLAB Graphics:

5.1 Two-Dimensional Line Plots

5.1.1 Problem

5.1.2 Solution

5.1.3 How It Works

5.2 General 2D Graphics

5.2.1 Problem

5.2.2 Solution

5.2.3 How It Works

5.3 Custom 2D Diagrams

5.3.1 Problem

5.3.2 Solution

5.3.3 How It Works

5.4 Three-Dimensional Box

5.4.1 Problem

5.4.2 Solution

5.4.3 How It Works

5.5 Draw a 3D Object with a Texture

5.5.1 Problem

5.5.2 Solution

5.5.3 How It Works

5.6 General 3D Graphics

5.6.1 Problem

5.6.2 Solution

5.6.3 How It Works

5.7 Building a Graphical User Interface

5.7.1 Problem

5.7.2 Solution

5.7.3 How It Works

Summary

Chapter 6:Machine Learning Examples in MATLAB

6.1 Introduction

6.2 Machine Learning

6.2.1 Neural Networks

6.2.2 Face Recognition

6.2.3 Data Classification

6.3 Control

6.3.1 Kalman Filters

6.3.2 Adaptive Control

6.4 Artificial Intelligence

6.4.1 Autonomous Driving and Target Tracking

Chapter 7:Face Recognition with Deep Learning

7.1 Obtain Data Online: For Training a Neural Network

7.1.1 Problem

7.1.2 Solution

7.1.3 How It Works

7.2 Generating Data for Training a Neural Net

7.2.1 Problem

7.2.2 Solution

7.2.3 How It Works

7.3 Convolution

7.3.1 Problem

7.3.2 Solution

7.3.3 How It Works

7.4 Convolution Layer

7.4.1 Problem

7.4.2 Solution

7.4.3 How It Works

7.5 Pooling

7.5.1 Problem

7.5.2 Solution

7.5.3 How It Works

7.6 Fully Connected Layer

7.6.1 Problem

7.6.2 Solution

7.6.3 How It Works

7.7 Determining the Probability

7.7.1 Problem

7.7.2 Solution

7.7.3 How It Works

7.8 Test the Neural Network

7.8.1 Problem

7.8.2 Solution

7.8.3 How It Works

7.9 Recognizing an Image

7.9.1 Problem

7.9.2 Solution

7.9.3 How It Works

Summary

Reference

Chapter 8:Data Classification

8.1 Generate Classification Test Data

8.1.1 Problem

8.1.2 Solution

8.1.3 How It Works

8.2 Drawing Decision Trees

8.2.1 Problem

8.2.2 Solution

8.2.3 How It Works

8.3 Decision Tree Implementation

8.3.1 Problem

8.3.2 Solution

8.3.3 How It Works

8.4 Implementing a Decision Tree

8.4.1 Problem

8.4.2 Solution

8.4.3 How It Works

8.5 Creating a Hand-Made Decision Tree

8.5.1 Problem

8.5.2 Solution

8.5.3 How It Works

8.6 Training and Testing the Decision Tree

8.6.1 Problem

8.6.2 Solution

8.6.3 How It Works

Summary

Reference

Chapter 9:Classification of Numbers Using Neural Networks

9.1 Generate Test Images with Defects

9.1.1 Problem

9.1.2 Solution

9.1.3 How It Works

9.2 Create the Neural Net Tool

9.2.1 Problem

9.2.2 Solution

9.2.3 How It Works

9.3 Train a Network with One Output Node

9.3.1 Problem

9.3.2 Solution

9.3.3 How It Works

9.4 Testing the Neural Network

9.4.1 Problem

9.4.2 Solution

9.4.3 How It Works

9.5 Train a Network with Multiple Output Nodes

9.5.1 Problem

9.5.2 Solution

9.5.3 How It Works

Summary

References

Chapter 10:Kalman Filters

10.1 A State Estimator

10.1.1 Problem

10.1.2 Solution

10.1.3 How It Works

10.1.4 Conventional Kalman Filter

10.2 Using the Unscented Kalman Filter for StateEstimation

10.2.1 Problem

10.2.2 Solution

10.2.3 How It Works

10.3 Using the UKF for Parameter Estimation

10.3.1 Problem

10.3.2 Solution

10.3.3 How It Works

Summary

References

Chapter 11:Adaptive Control

11.1 Self-Tuning: Finding the Frequency of an Oscillator

11.1.1 Problem

11.1.2 Solution

11.1.3 How It Works

11.2 Model Reference Adaptive Control

11.2.1 Generating a Square Wave Input

11.2.1.1 Problem

11.2.1.2 Solution

11.2.1.3 How It Works

11.2.2 Implement Model Reference Adaptive Control

11.2.2.1 Problem

11.2.2.2 Solution

11.2.2.3 How It Works

11.2.3 Demonstrate MRAC for a Rotor

11.2.3.1 Problem

11.2.3.2 Solution

11.2.3.3 How It Works

11.3 Longitudinal Control of an Aircraft

11.3.1 Write the Differential Equations for the LongitudinalMotion of an Aircraft

11.3.1.1 Problem

11.3.1.2 Solution

11.3.1.3 How It Works

11.3.2 Numerically Finding Equilibrium

11.3.2.1 Problem

11.3.2.2 Solution

11.3.2.3 How It Works

11.3.3 Numerical Simulation of the Aircraft

11.3.3.1 Problem

11.3.3.2 Solution

11.3.3.3 How It Works

11.3.4 Find a Limiting and Scaling function for a Neural Net

11.3.4.1 Problem

11.3.4.2 Solution

11.3.4.3 How It Works

11.3.5 Find a Neural Net for the Learning Control

11.3.5.1 Problem

11.3.5.2 Solution

11.3.5.3 How It Works

11.3.6 Enumerate All Sets of Inputs

11.3.6.1 Problem

11.3.6.2 Solution

11.3.6.3 How It Works

11.3.7 Write a General Neural Net Function

11.3.7.1 Problem

11.3.7.2 Solution

11.3.7.3 How It Works

11.3.8 Implement PID Control

11.3.8.1 Problem

11.3.8.2 Solution

11.3.8.3 How It Works

11.3.9 Demonstrate PID control of Pitch for the Aircraft

11.3.9.1 Problem

11.3.9.2 Solution

11.3.9.3 How It Works

11.3.10 Create the Neural Net for the Pitch Dynamics

11.3.10.1 Problem

11.3.10.2 Solution

11.3.10.3 How It Works

11.3.11 Demonstrate the Controller in a Nonlinear Simulation

11.3.11.1 Problem

11.3.11.2 Solution

11.3.11.3 How It Works

11.4 Ship Steering: Implement Gain Scheduling for Steering Control of a Ship

11.4.1 Problem

11.4.2 Solution

11.4.3 How It Works

Summary

References

Chapter12:Autonomous Driving

12.1 Modeling the Automobile Radar

12.1.1 Problem

12.1.2 How It Works

12.1.3 Solution

12.2 Automobile Autonomous Passing Control

12.2.1 Problem

12.2.2 Solution

12.2.3 How It Works

12.3 Automobile Dynamics

12.3.1 Problem

12.3.2 How It Works

12.3.3 Solution

12.4 Automobile Simulation and the Kalman Filter

12.4.1 Problem

12.4.2 Solution

12.4.3 How It Works

12.5 Perform MHT on the Radar Data

12.5.1 Problem

12.5.2 Solution

12.5.3 How It Works

12.5.4 Hypothesis Formation

12.5.4.1 Problem

12.5.4.2 Solution

12.5.4.3 How It Works

12.5.5 Track Pruning

12.5.5.1 Problem

12.5.5.2 Solution

12.5.5.3 How It Works

12.5.5.4 Simulation

Summary

References

Index