Machine Learning with MATLAB

MATLAB for Machine Learning

Train models, tune parameters, and deploy to production or the edge

Using MATLAB^®, engineers and other domain experts have deployed thousands of machine learning applications. MATLAB makes the hard parts of machine learning easy with:

Point-and-click apps for training and comparing models
Advanced signal processing and feature extraction techniques
Automatic machine learning (AutoML) including feature selection, model selection and hyperparameter tuning
The ability to use the same code to scale processing to big data and clusters
Automated generation of C/C++ code for embedded and high-performance applications
Integration with Simulink as native or MATLAB Function blocks, for embedded deployment or simulations
All popular classification, regression, and clustering algorithms for supervised and unsupervised learning
Faster execution than open source on most statistical and machine learning computations

ARTICLE SERIES

Machine Learning and Deep Learning Q&A

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AI Applications with MATLAB and Simulink

Check out a variety of AI applications in automated driving, robotics, computer vision, and more.

Interactive Apps and Algorithms

Choose from a wide variety of the most popular classification, clustering, and regression algorithms – now also “shallow” neural nets (up to three layers) alongside other machine learning models. Use classification and regression apps to interactively train, compare, tune, and export models for further analysis, integration, and deployment. If writing code is more your style, you can further optimize models with feature selection and parameter tuning.

Model Interpretability

Overcome the black-box nature of machine learning by applying established interpretability methods such as Partial Dependence plots, LIME, Shapley values, and Generalized Additive Model (GAM). Validate that the model is using the right evidence for its predictions and find model biases that were not apparent during training.

Automated Machine Learning (AutoML)

Automatically generate features from training data and optimize models using hyperparameter tuning techniques such as Bayesian optimization. Use specialized feature extraction techniques such as wavelet scattering for signal or image data, and feature selection techniques such as neighborhood component analysis (NCA), minimum redundancy maximum relevance (MRMR) or sequential feature selection.

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Code Generation and Simulink Integration

Deploy statistics and machine learning models to embedded systems and generate readable C or C++ code for your entire machine learning algorithm, including pre and post processing steps. Accelerate verification and validation of your high-fidelity simulations using machine learning models through MATLAB function blocks and native blocks in Simulink.

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Using a machine learning model in Simulink to accept streaming data and predict the label and classification score with an SVM model.

Scaling & Performance

Use tall arrays train machine learning models to data sets too large to fit in memory, with minimal changes to your code. You can also speed up statistical computations and model training with parallel computing on your desktop, on clusters, or on the cloud.

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