Machine Learning with MATLAB

Explore how MATLAB transforms the world of machine learning. Discover five areas where MATLAB can help solve diverse learning problems. From interactive apps to Simulink integration, we’ve got you covered.

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Investors have been dreaming of creating a machine that thinks and learns since the time of ancient Greece. Today, we live in a world where machine learning has gone from a dream to one of the most important areas within computer science. It is so popular that you can find materials about it virtually everywhere. However, only a few of those materials are worthy of your time.

In this blog, we have collected five areas where MATLAB can help solve a wide range of learning problems when it comes to machine learning.

Being a strong environment for interactive exploration, MATLAB provides essential tools for solving machine-learning problems. Put simply, MATLAB makes the hard parts of machine learning easy.

Interactive Apps and Algorithms


Interactive Apps and Algorithms

Choose from a range of classification, clustering, and regression algorithms, including “shallow” neural nets (up to three layers), among other machine learning models. Use classification and regression apps to interactively train, compare, tune, and export models for further analysis, integration, and deployment. If you prefer to write code, feature selection and parameter tuning can help you improve models even more.

Model Interpretability


Use known interpretability methods (Shapley values, generalized additive models, LIME, and partial dependency graphs) to overcome the problematic black-box nature of machine learning. Verify that the model is making predictions with the right evidence, and look for model biases that were not obvious during training.

 Contact Us Model Interpretability

Automated Machine Learning


Automated Machine Learning

Using hyperparameter tuning approaches such as Bayesian optimization, it automatically generates features from training data and optimizes models. For signal or picture data and feature selection techniques (neighborhood component analysis (NCA), minimum redundancy maximum relevance (MRMR), and sequential feature selection), use specific feature extraction techniques (wavelet scattering).

Simulink® Integration and Code Generation


Machine learning models through MATLAB function blocks and native Simulink blocks will help you verify and validate your high-fidelity simulations faster. With the help of statistics and machine learning models, you can generate C or C++ code for the whole machine learning algorithm, including pre- and post-processing steps.

Simulink Integration and Code Generation

Scaling & Performance


Scaling & Performance

With minimal code changes, tall arrays that train machine learning models can help you fit large data sets into memory. Parallel computing on your desktop, in clusters, or in the cloud can help you speed up statistical computations and model training.

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All products mentioned in this user story are developed by MathWorks.

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