# Introduction to Machine Learning with Matlab

## Course Overview

**Video: Machine Learning with Matlab**

## Course Example - Basketball Player Statistics

## Getting Starting with Data

**Exercise: Using Logical Indexing**

**Exercise: Creating Categorical Data**

**Exercise: Merging and Visualizing Data**

**Exercise: Merging and Visualizing Data**

**Exercise: Basketball Statistics Script**

# Finding Natural Patterns in the Data

## Clustering Basketball Players

**Video: Clustering Basketball Players**

## Low Dimensional Visualization

To effectively visualize the data containing more than three predictors, you can use the dimensionality reduction techniques such as multidimensional scaling and principal component analysis (PCA).

**Exercise: Classical Multidimensional Scaling**

**Exercise: Nonclassical Multidimensional Scaling**

## k-Means Clustering

**Video: What is k-Means Clustering**

**Exercise: Options for k-Means Clustering**

## Gaussian Mixture Models

**Video: What are Gaussian Mixture Models**

## Interpreting the Clusters

## Hierarchical Clustering

**Video: Hierarchical Clustering**

**Exercise: Determine Hierarchical Structure**

**Exercise: Divide Hierarchical Tree into Clusters**

# Building Classification Models

## Course Example: Heart Disease

**Video: Heart Disease Classification**

## Preparing Data

**Video: Training and Validation Data**

**Exercise: Making Training and Test Sets**

## Fitting and Predicting

**Exercise: Fitting and Predicting**

**Exercise: Using a Classification Variable**

## Evaluating the Classification

**Exercise: Prediction and Resubstitution Loss**

**Exercise: Confusion and Cost Matrix**

# Classification Methods

## Course Example: Different Methods to Classify Heart Patients

**Video: Classification Learner App**

**Exercise: Classification Learner App**

## Nearest Neighbor Classification

**Exercise: Using Nearest Neighbor Classification with Tables**

## Classification Trees

**Video: What is a Classification Tree?**

**Exercise: Using Classification Trees**

**Exercise: Heart Health – Numeric Data**

**Exercise: Heart Health – Numeric and Categorical Data**

## Naive Bayes Classification

**Exercise: Using Naive Bayes Classification**

**Exercise: Heart Health – Numeric Data**

**Exercise: Heart Health – Numeric and Categorical Data**

## Discriminant Analysis

**Video: What is Discriminant Analysis??**

**Exercise: Using Discriminant Analysis**

## Support Vector Machines

**Video: What are Support Vector Machines?**

**Exercise: Using Support Vector Machine Classification**

**Exercise: Heart Health – Numeric Data**

**Exercise: Heart Health – Numeric and Categorical Data**

## Multiclass Support Vector Machines

**Exercise: Using Multiclass Support Vector Machine Classification**

**Exercise: Heart Health – Numeric Data**

**Exercise: Heart Health – Numeric and Categorical Data**

# Improving Predictive Models

## Methods for Improving Predictive Models

## Cross Validation

**Video: What is Cross Validation?**

## Reducing Predictors - Feature Transformation

## Reducing Predictors - Feature Selection

**Exercise: Built-in Feature Selection**

**Exercise: Heart Health – Built-in Feature Selection**

**Exercise: Sequential Feature Selection**

**Exercise: Heart Health – Sequential Feature Selection**

**Exercise: Creating Dummy Variables**

**Exercise: Heart Health – Feature Selection with Categorical Data**

## Ensemble Learning

# Building Regression Models

## Course Example - Fuel Economy

## Linear Models

**Video: What is Linear Regression?**

**Exercise: Fitting a Polynomial**

**Exercise: Multivariable Linear Regression**

**Exercise: Multivariable Linear Regression with Numeric Arrays**

## SVMS and Trees

**Exercise: Using Tree and SVM Models**

**Exercise: Choosing a Regression Model**

## Gaussian Process Regression

**Video: What is Gaussian Process Regression?**

**Exercise: Using GPR with Outliers**

## Regularized Linear Models

**Exercise: Fuel Economy – Ridge Regression**

**Exercise: Fuel Economy – Lasso Regression**

## Stepwise Fitting

**Exercise: Stepwise Feature Selection**

# Creating Neural Networks

## Overview of Neural Networks

**Video: What are Neural Networks?**

## Self-Organizing Maps

**Video: What are Self-Organizing Maps?**

**Video: Interactively Creating SOMs**

**Exercise: Interactively Creating SOMs**

**Exercise: Using Commands to Create SOMs **

## Feed-Forward Networks

**Video: What are Feed-Forward Networks?**

**Video: Interactively Creating Feed-Forward Networks**

**Exercise: Interactively Creating Feed-Forward Classification Networks**

# Additional Resources

**Videos: Introduction to Machine Learning**

**Videos: Applied Machine Learning**

**Machine Learning Quick Start Guide**

**Machine Learning Workflow E-Book**