Machine Learning with MATLAB

Angelika Teglasy

Learn about MATLAB support for machine learning so you can train your models, tune parameters, and deploy it to production.

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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 investments of your time.

This blog is created for everyone interested in Machine Learning.  Our goal is to collect the best content on the web on Machine Learning in MATLAB and help all users to harness the power of MATLAB to solve a wide range of learning problems.

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.

MATLAB for Machine Learning

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

 Contact Us Model Interpretability

Use known interpretability methods (Shapley values, Generalized Additive Model, LIME, Partial Dependence 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.

Automated Machine Learning

Automated Machine Learning

Using hyperparameter tuning approaches like Bayesian optimization, 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), Sequential Feature Selection), use specific feature extraction techniques (Wavelet Scattering).

Simulink Integration and Code Generation

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.

Scaling & Performance

Scaling & Performance

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

 

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