MATLAB for Machine Learning

Table of contents

1. Preface
   1. What this book covers
   2. What you need for this book
   3. Who this book is for
   4. Conventions
   5. Reader feedback
   6. Customer support
      1. Downloading the example code
      2. Errata
      3. Piracy
      4. Questions
2. Getting Started with MATLAB Machine Learning
   1. ABC of machine learning
   2. Discover the different types of machine learning
      1. Supervised learning
      2. Unsupervised learning
      3. Reinforcement learning
   3. Choosing the right algorithm
   4. How to build machine learning models step by step
   5. Introducing machine learning with MATLAB
      1. System requirements and platform availability
      2. MATLAB ready for use
   6. Statistics and Machine Learning Toolbox
      1. Datatypes
         1. Supported datatypes
         2. Unsupported datatypes
      2. What can you do with the Statistics and Machine Learning Toolbox?
         1. Data mining and data visualization
         2. Regression analysis
         3. Classification
         4. Cluster analysis
         5. Dimensionality reduction
   7. Neural Network Toolbox
   8. Statistics and algebra in MATLAB
   9. Summary
3. Importing and Organizing Data in MATLAB
   1. Familiarizing yourself with the MATLAB desktop
   2. Importing data into MATLAB
      1. The Import Wizard
      2. Importing data programmatically
         1. Loading variables from file
         2. Reading an ASCII-delimited file
         3. Comma-separated value files
         4. Importing spreadsheets
         5. Reading mixed strings and numbers
   3. Exporting data from MATLAB
   4. Working with media files
      1. Handling images
      2. Sound import/export
   5. Data organization
      1. Cell array
      2. Structure array
      3. Table
      4. Categorical array     
   6. Summary
4. From Data to Knowledge Discovery
   1. Distinguishing the types of variables
      1. Quantitative variables
      2. Qualitative variables
   2. Data preparation
      1. A first look at data
      2. Finding missing values
      3. Changing the datatype
      4. Replacing the missing value
      5. Removing missing entries
      6. Ordering the table
      7. Finding outliers in data
      8. Organizing multiple sources of data into one
   3. Exploratory statistics - numerical measures
      1. Measures of location
         1. Mean, median, and mode
         2. Quantiles and percentiles
      2. Measures of dispersion
      3. Measures of shape
         1. Skewness
         2. Kurtosis
   4. Exploratory visualization
      1. The Data Statistics dialog box
      2. Histogram
      3. Box plots
      4. Scatter plots
   5. Summary
5. Finding Relationships between Variables - Regression Techniques
   1. Searching linear relationships
      1. Least square regression
      2. The Basic Fitting interface
   2. How to create a linear regression model
      1. Reducing outlier effects with robust regression
      2. Multiple linear regression
         1. Multiple linear regression with categorical predictor
   3. Polynomial regression
   4. Regression Learner App
   5. Summary
6. Pattern Recognition through Classification Algorithms
   1. Predicting a response by decision trees
   2. Probabilistic classification algorithms - Naive Bayes
      1. Basic concepts of probability
      2. Classifying with Naive Bayes
      3. Bayesian methodologies in MATLAB
   3. Describing differences by discriminant analysis
   4. Find similarities using nearest neighbor classifiers
   5. Classification Learner app
   6. Summary
7. Identifying Groups of Data Using Clustering Methods
   1. Introduction to clustering
      1. Similarity and dissimilarity measures
      2. Methods for grouping objects
         1. Hierarchical clustering
         2. Partitioning clustering
   2. Hierarchical clustering
      1. Similarity measures in hierarchical clustering
      2. Defining a grouping in hierarchical clustering
      3. How to read a dendrogram
      4. Verifying your hierarchical clustering
   3. Partitioning-based clustering methods - K-means algorithm
      1. The K-means algorithm
      2. The kmeans() function
      3. The silhouette plot
   4. Partitioning around the actual center - K-medoids clustering
      1. What is a medoid?
      2. The kmedoids() function
      3. Evaluating clustering
   5. Clustering using Gaussian mixture models
      1. Gaussian distribution
      2. GMM in MATLAB
      3. Cluster membership by posterior probabilities
   6. Summary
8. Simulation of Human Thinking - Artificial Neural Networks
   1. Getting started with neural networks
   2. Basic elements of a neural network
      1. The number of hidden layers
      2. The number of nodes within each layer
      3. The network training algorithm
   3. Neural Network Toolbox
   4. A neural network getting started GUI
   5. Data fitting with neural networks
      1. How to use the Neural Fitting app (nftool)
      2. Script analysis
   6. Summary
9. Improving the Performance of the Machine Learning Model - Dimensionality Reduction
   1. Feature selection
      1. Basics of stepwise regression
      2. Stepwise regression in MATLAB
   2. Feature extraction
      1. Principal Component Analysis
   3. Summary
10. Machine Learning in Practice
    1. Data fitting for predicting the quality of concrete
    2. Classifying thyroid disease with a neural network
    3. Identifying student groups using fuzzy clustering
    4. Summary