Latest Features

Learn about the latest MATLAB features for machine learning

Interactive Apps

  • Use the Classification Learner app to interactively explore data, select features, and train and evaluate supervised classification models
  • New Leverage the Regression Learner app to interactively train regression models
  • 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

The Classification Learner app lets you train models to classify data using supervised machine learning.
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Classify Data Using the Classification Learner App
Use the power and simplicity of tall arrays in MATLAB® to access, explore, and process big data, as well as data that doesn't fit in memory (including data stored on Spark™ and Hadoop®).
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MATLAB Tall Arrays in Action

Big Data 

  • Use tall arrays with many classification, regression, and clustering algorithms to train models on data sets that do not fit in memory. 
  • Minimize latencies by delaying the processing of complete datasets
  • New Use fit kernel SVM regression and classification models with tall arrays
  • New Use fast approximate means, quantiles, and non-stratified partitions on out-of-memory data

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

Automated Model Optimization

  • Automatically tune hyperparameters using Bayesian optimization
  • Automatically select a subset of relevant features using techniques like neighborhood component analysis (NCA)
  • New Perform unsupervised feature learning using sparse filtering and reconstruction independent component analysis (RICA)
  • Parallelize the execution of automated optimization methods on multiple cores using Parallel Computing Toolbox, and scale to clouds and clusters using MATLAB Distributed Computing Server

Related Products: MATLAB Distributed Computing Server, Parallel Computing Toolbox, Statistics and Machine Learning Toolbox

Deployment 

  • Automatically generate C/C++ code for many popular classification, regression, and clustering algorithms
  • New Generate C code for distance calculations on vectors and matrices, and for prediction by using k-nearest neighbor and nontree ensemble models

Related Products: MATLAB Coder, MATLAB Compiler, 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
  • New Use advanced dimensionality reduction algorithms like Stochastic Neighbor Embedding (t-SNE)
  • New Visualize high-density data with improved scatter plots in the Classification Learner app

Related Products: Statistics and Machine Learning Toolbox

Comparison of MATLAB® with Microsoft® R Open (3.4.1) and the Intel® Distribution for Python (2018) across several general programming and machine learning tasks.

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 
  • New Use popular clustering algorithms including k-means, k-mediods, hierarchical clustering, Gaussian mixture, and Hidden Markov models
  • Run statistical and machine learning computations faster than with open-source tools 

Related Products: Statistics and Machine Learning Toolbox

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