Latest Features

Learn about the latest MATLAB features for machine learning

Interactive Apps

  • Use the Classification Learner and Regression Learner apps to interactively explore data, select features, and train and evaluate supervised classification and regression models
  • New Perform automated tuning of hyperparameters and apply cost matrices from within the learner apps
  • Fit data to a wide range of probability distributions and explore the effects of changing parameter values using the Distribution Fitter app

Related Products: Statistics and Machine Learning Toolbox

Automated Model Optimization

  • New Optimize model type and hyperparameters simultaneously
  • Automatically tune hyperparameters using Bayesian optimization
  • Automatically select a subset of relevant features using techniques like neighborhood component analysis (NCA) and feature ranking
  • Parallelize the execution of automated optimization methods on multiple cores using Parallel Computing Toolbox, and scale to clouds and clusters using MATLAB Parallel Server

Related Products: MATLAB Parallel ServerParallel Computing ToolboxStatistics and Machine Learning Toolbox

Machine Learning and Statistical Algorithms 

  • Leverage commonly used algorithms for classification and regression, such as linear and generalized linear models, support vector machines, decision trees, ensemble methods, and more 
  • Use popular clustering algorithms including k-means, k-mediods, hierarchical clustering, Gaussian mixture, and Hidden Markov models
  • New Use density-based spatial clustering of applications with noise (DBSCAN) and spectral clustering of arbitrary shapes
  • Run statistical and machine learning computations faster than with open-source tools

Related Products: Statistics and Machine Learning Toolbox

Data Visualization

  • Explore the structure of your data and relationships between features through scatter plots, box plots, dendrograms, and other standard statistical visualizations
  • Use advanced dimensionality reduction algorithms like Stochastic Neighbor Embedding (t-SNE)
  • Visualize high-density data with improved scatter plots in the Classification Learner app
  • New Create confusion matrices from tall arrays

Related Products: Statistics and Machine Learning Toolbox


  • Automatically generate C/C++ code for many popular classification, regression, and clustering algorithms
  • New Deploy to devices with limited memory and/or power using fixed-point arithmetic
  • New Update parameters of deployed models such as SVM, linear models, and decision trees, without regenerating C/C++ prediction code

Related Products: MATLAB Coder, MATLAB Compiler, Statistics and Machine Learning Toolbox

Big Data 

  • Use tall arrays with many classification, regression, and clustering algorithms to train models on data sets that do not fit in memory
  • Fit multiclass classification models, perform hyperparameter optimization, and specify cost with tall arrays
  • Use fast approximate means, quantiles, and non-stratified partitions on out-of-memory data

Related Products: Parallel Computing Toolbox, Statistics and Machine Learning Toolbox