Brought to you by our collaborators...

• UNIPEN: Our first large dataset of on-line handwriting collected by the iUF.
• CLOP: An object-oriented Matlab(R) library of machine learning algorithms having performed well in past challenges, including ridge regression, SVMs, boosting, and random forests. It provides an interface to Weka and R.
• GROP: Gesture Recognition Object Package. Software used in the gesture recognition challenge.
• GLOP: A Matlab (R) package providing models included in the Virtual Lab of the Causality Workbench. Partially based on the Bayes Net Toolbox.
• Causal explorer: A causal probabilistic network learning toolkit for Matlab (R). This package supports "local" causal discovery algorithms, efficient to discover the causal structure around a target variable, even for a large number of variables.
• TETRAD: A standalone program for creating, simulating data from, estimating, testing, predicting with, and searching for causal/statistical models.

Other resources:

• Scikit-learn: A Python-based machine learning toolkit, and scikit-feature selection, a feature selection toolkit.
• R: A software environment for statistical computing including many machine learning algorithms.
• Weka: A standalone data mining environment written in Java.
• Kernel methods: The kernel machines website provides a list of software for kernel methods, including SVMs. The GKM toolbox provides tools for building generalized kernel machines.
• MLOSS: Machine Learning Open Source Software (software repository of JMLR).
• Software Packages for Machine Learning: A list of machine learning software maintained by David Aha. Commercial packages are found on the KDnuggets website.
• Software Packages for Graphical Models: A list maintained by Kevin Murphy of links to software for graphical models/Bayes networks.
• Machine Learning Tutorials: A list maintained by the AIPR.
• On-line Machine Learning Courses: A list maintained by CourseDuck.