Machine Learning with MATLAB

Who Should Attend?


Duration: 2 Days

The course demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlights techniques for visualization and evaluation of results.

  • 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

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