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
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 Parallel Server
Related Products: MATLAB Parallel 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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