Latest Features for Machine Learning

Latest Features

Learn about the latest MATLAB features for machine learning

Interactive Apps
Big Data
Automated Model Optimization

Deployment
Data Visualization
Machine Learning and Statistical Algorithms

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

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 Parallel Server

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

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