Machine Learning with MATLAB

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  • Machine Learning with MATLAB

    Build predictive models and discover useful patterns from
    observed data.

  • Machine Learning with MATLAB Webinar

    Learn how to get started using machine learning tools to
    detect patterns and build predictive models from your data sets.

  • Get started with examples for classification, regression, and clustering

Machine learning algorithms use computational methods to “learn” information directly from data without assuming a predetermined equation as a model. They can adaptively improve their performance as you increase the number of samples available for learning.

Machine learning algorithms are used in applications such as computational finance (credit scoring and algorithmic trading), computational biology (tumor detection, drug discovery, and DNA sequencing), energy production (price and load forecasting), natural language processing, speech and image recognition, and advertising and recommendation systems.

Machine learning is often used in big data applications, which have large datasets with many predictors (features) and are too complex for a simple parametric model.  Examples of big data applications include forecasting electricity load with a neural network, or bond rating classification for credit risk using an ensemble of decision trees.

Classification for machine learning


Build models to classify data into different categories.

Regression for machine learning


Build models to predict continuous data.

Clustering for machine learning


Find natural groupings and patterns in data.

For more information on solving machine learning problems, see Statistics Toolbox™, Neural Network Toolbox™ and Fuzzy Logic Toolbox™.

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