• Organizing and preprocessing data
• Clustering data
• Creating classification models
• Interpreting and evaluating models
• Simplifying data sets
• Using ensembles to improve model performance

DAY 1 OF 2

Importing and Organizing Data

Objectives: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.

• Data types
• Tables
• Categorical data
• Data preparation

Finding Natural Patterns in Data

Objectives: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.

• Unsupervised learning
• Clustering methods
• Cluster evaluation and interpretation

Building Classification Models

Objectives: Use supervised learning techniques to perform predictive modelling for classification problems.  Evaluate the accuracy of a predictive model.

• Supervised learning
• Training and validation
• Classification methods

DAY 2 OF 2

Improving Predictive Models

Objectives: Reduce the dimensionality of a data set. Improve and simplify machine learning models.

• Cross validation
• Feature transformation
• Feature selection
• Ensemble learning

Building Regression Models

Objectives: Use supervise learning techniques to perform predictive modeling for continuous response variables.

• Parametric regression methods
• Nonparametric regression methods
• Evaluation of regression models

Creating Neural Networks

Objectives: Create ad train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.

• Clustering with Self-Organizing Maps
• Classification with feed-forward networks
• Regression with feed-forward networks