Open Source MATLAB Machine Learning Software
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MATLAB Machine Learning Software
View 445 business solutionsBrowse free open source MATLAB Machine Learning Software and projects below. Use the toggles on the left to filter open source MATLAB Machine Learning Software by OS, license, language, programming language, and project status.
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads: http://arma.sourceforge.net/download.html * Documentation: http://arma.sourceforge.net/docs.html * Bug reports: http://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/conradsnicta/armadillo-code -
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Coursera Machine Learning
Coursera Machine Learning By Prof. Andrew Ng
CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes week-by-week course schedules with links to exercises, lecture notes, and additional resources. Alongside the official coursework, the repository includes supplemental explanations, code snippets, and references to recommended textbooks and external materials. By gathering course-related resources into a single space, this project acts as a practical study companion for learners revisiting or supplementing the original course. -
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OpenFace
A state-of-the-art facial behavior analysis toolkit
OpenFace is an advanced facial behavior analysis toolkit intended for computer vision and machine learning researchers, those in the affective computing community, and those who are simply interested in creating interactive applications based on facial behavior analysis. The OpenFace toolkit is capable of performing several complex facial analysis tasks, including facial landmark detection, eye-gaze estimation, head pose estimation and facial action unit recognition. OpenFace is able to deliver state-of-the-art results in all of these mentioned tasks. OpenFace is available for Windows, Ubuntu and macOS installations. It is capable of real-time performance and does not need to run on any specialist hardware, a simple webcam will suffice. -
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DeepLearnToolbox
Matlab/Octave toolbox for deep learning
DeepLearnToolbox is a MATLAB / Octave toolbox for prototyping deep learning models. It provides implementations of feedforward neural networks, convolutional neural networks (CNNs), deep belief networks (DBNs), stacked autoencoders, convolutional autoencoders, and more. The toolbox includes example scripts for each method, enabling users to quickly experiment with architectures, training, and inference workflows. Although it's been flagged as deprecated and no longer actively maintained, it is still used for educational and prototyping purposes. Deep belief networks (DBN) and restricted Boltzmann machines (RBM). Example scripts demonstrating usage. -
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BPL
Bayesian Program Learning model for one-shot learning
BPL (Bayesian Program Learning) is a MATLAB implementation of the Bayesian Program Learning framework for one-shot concept learning (especially on handwritten characters). The approach treats each concept (e.g. a character) as being generated by a probabilistic program (motor primitives, strokes, spatial relationships), and inference proceeds by fitting those generative programs to a single example, generalizing to new examples, and generating new exemplars. The repository contains code for parsing stroke sequences, fitting motor programs, exemplar generation, classification, re-fitting, and demonstration scripts. -
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Eventer
Rapid, unbiased, reproducible analysis of synaptic events
Eventer is a programme designed for the detection of spontaneous synaptic events measured by electrophysiology or imaging. The software combines deconvolution for detection, and variable length template matching approaches for screening out false positive events. Eventer also includes a machine learning-based approach allowing users to train a model to implement their ‘expert’ selection criteria across data sets without bias. Sharing models allows users to implement consistent analysis procedures. The software is coded in MATLAB, but has been compiled as standalone applications for Windows, Mac and Linux. Please visit the official Eventer website for more info https://eventerneuro.netlify.app/ While the paper is in preparation, please cite as; Winchester, G., Liu, S., Steele, O.G., Aziz, W. and Penn, A.C. (2020) Eventer. Software for the detection of spontaneous synaptic events measured by electrophysiology or imaging. http://doi.org/10.5281/zenodo.3991676 -
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MatlabMachine
Machine learning algorithms
Matlab-Machine is a comprehensive collection of machine learning algorithms implemented in MATLAB. It includes both basic and advanced techniques for classification, regression, clustering, and dimensionality reduction. Designed for educational and research purposes, the repository provides clear implementations that help users understand core ML concepts. -
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Stanford Machine Learning Course
machine learning course programming exercise
The Stanford Machine Learning Course Exercises repository contains programming assignments from the well-known Stanford Machine Learning online course. It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. Each folder corresponds to a specific algorithm or concept, making it easy for learners to navigate and practice. The exercises serve as practical, hands-on reinforcement of theoretical concepts taught in the course. This collection is valuable for students and practitioners who want to strengthen their skills in machine learning through coding exercises. -
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mTRF-Toolbox
A MATLAB package for modelling multivariate stimulus-response data
mTRF-Toolbox is a MATLAB package for modelling multivariate stimulus-response data, suitable for neurophysiological data such as MEG, EEG, sEEG, ECoG and EMG. It can be used to model the functional relationship between neuronal populations and dynamic sensory inputs such as natural scenes and sounds, or build neural decoders for reconstructing stimulus features and developing real-time applications such as brain-computer interfaces (BCIs). Toolbox Paper: http://dx.doi.org/10.3389/fnhum.2016.00604 Methods Paper: https://doi.org/10.3389/fnins.2021.705621 -
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JAABA
The Janelia Automated Animal Behavior Annotator
The Janelia Automatic Animal Behavior Annotator (JAABA) is a machine learning-based system that enables researchers to automatically compute interpretable, quantitative statistics describing video of behaving animals. Through our system, users encode their intuition about the structure of behavior by labeling the behavior of the animal, e.g. walking, grooming, or following, in a small set of video frames. JAABA uses machine learning techniques to convert these manual labels into behavior detectors that can then be used to automatically classify the behaviors of animals in large data sets with high throughput. JAABA combines an intuitive graphical user interface, a fast and powerful machine learning algorithm, and visualizations of the classifier into an interactive, usable system for creating automatic behavior detectors. Documentation is available at: http://jaaba.sourceforge.net/ -
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nn22 Basic Neural Networks for Octave
Simple .m files, Basic Neural Networks study for Octave (or Matlab)
--> For a more detailed description check the README text under the 'Files' menu option :) The project consists of a few very simple .m files for a Basic Neural Networks study under Octave (or Matlab). The idea is to provide a context for beginners that will allow to develop neural networks, while at the same time get to see and feel the behavior of a basic neural networks' functioning. The code is completely open to be modified and may suit several scenarios. The code commenting is verbose, and variables and functions do respect English formatting, so that code may be self explanatory. Messages to the screen are localized, both in English and Spanish, and it is really easy to add another language to the localization. If any further explanation is needed, the forum/discussion page may be of help :) Comments and suggestions are very welcome. -
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This site contains four packages of Mass and mass-based density estimation. 1. The first package is about the basic mass estimation (including one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation). This packages contains the necessary codes to run on MATLAB. 2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system. 3. The third package DEMassBayes includes the source and object files of a Bayesian classifier using DEMass. DEMassBayes.7z has jar file to be used with WEKA and a readme file listing parameters used. The source files are included in DEMassBayes_Source.7z. 4. The four package is MassTER includes source and JAR file to be used with WEKA system..
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CometAnalyser
CometAnalyser, for quantitative comet assay analysis.
Description: Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. To obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. CometAnalyser is an open-source deep-learning tool designed for the analysis of both fluorescent and silver-stained wide-field microscopy images. Once the comets are segmented and classified, several intensity/morphological features are automatically exported as a spreadsheet file. Video Tutorial: CometAnalyser is written in MATLAB. It works with Windows, Macintosh, and UNIX-based systems. Please, download the sample datasets and test it watching the video tutorial to understand how it works: https://www.youtube.com/watch?v=vh2VFnMw50A Contacts: filippo.piccinini85@gmail.com beleonattila@gmail.com -
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LWPR
Locally Weighted Projection Regression (LWPR)
Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite: [1] Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal, Incremental Online Learning in High Dimensions, Neural Computation, vol. 17, no. 12, pp. 2602-2634 (2005). [2] Stefan Klanke, Sethu Vijayakumar and Stefan Schaal, A Library for Locally Weighted Projection Regression, Journal of Machine Learning Research (JMLR), vol. 9, pp. 623--626 (2008). More details and usage guidelines on the code website. -
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A MATLAB spectral clustering package to handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. We implement various ways of approximating the dense similarity matrix, including nearest neighbors and the Nystrom method.
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Overhead Imagery Research Data Set (OIRDS) - an annotated data library & tools to aid in the development of computer vision algorithms
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DOGMA is a MATLAB toolbox for discriminative online learning. It implements all the state of the art algorithms in a unique and simple framework. Examples are Perceptron, Passive-Aggresive, ALMA, NORMA, SILK, Projectron, RBP, Banditron, etc.
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MCTIMME
Microbial Counts Trajectories Infinite Mixture Model Engine
MCTIMME is a nonparametric Bayesian computational framework for analyzing microbial time-series data.The current implementation is in Matlab. -
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Kernel Adaptive Filtering Toolbox
a Matlab benchmarking toolbox for kernel adaptive filtering
[Note: This project has moved. Visit https://github.com/steven2358/kafbox/ for the latest version.] A Matlab benchmarking toolbox for kernel adaptive filtering. Kernel adaptive filtering algorithms are online and adaptive regression algorithms based on kernels. They are suitable for nonlinear filtering, prediction, tracking and nonlinear regression in general. This toolbox includes algorithms, demos, and tools to compare their performance. See the included README file for a list of included algorithms and more details. If you use this toolbox in your research please cite: @inproceedings{vanvaerenbergh2013comparative, author = {Van Vaerenbergh, Steven and Santamar{\'i}a, Ignacio}, booktitle = {2013 IEEE Digital Signal Processing (DSP) Workshop and IEEE Signal Processing Education (SPE)}, title = {A Comparative Study of Kernel Adaptive Filtering Algorithms}, year = {2013}, note = {Software available at \url{https://github.com/steven2358/kafbox/}} } -
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Fingerprint Recognition System
Fingerprint Recognition System 5.3 - Matlab source code
The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information. Index Terms: Biometrics, FingerCode, fingerprints, flow pattern, Gabor filters, matching, texture, verification. -
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GI-ICA
Matlab implementation of GI-ICA and PEGI
This is a matlab implementation of the GI-ICA algorithm for ICA in the presence of an additive Gaussian noise. The algorithm is discussed in the paper "Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis" by James Voss, Luis Rademacher, and Mikhail Belkin. -
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KMBOX - Kernel Methods Toolbox
A collection of kernel-based algorithms for Matlab.
KMBOX is a collection of MATLAB programs that implement kernel-based algorithms, with a focus on regression algorithms and online algorithms. It can be used for nonlinear signal processing and machine learning. -
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lonestar
A feature selection and classification algorithm based on L1 Norm SVM
A feature selection and classification algorithm. It is based on L1 Norm Support Vector Machine with t-test and Recursive Feature Elimination.
