All about getting machines to learn to do something rather than explicitly programming to do it. Tends to deal with pattern matching a lot and are heavily math and statistically based. Technically Connectionism falls under this category, but it is such a large sub-field I'm keeping it in a separate section.
Libraries or frameworks used for writing machine learning systems.
CompLearn is a software system built to support compression-based learning in a wide variety of applications. It provides this support in the form of a library written in highly portable ANSI C that runs in most modern computer environments with minimal confusion. It also supplies a small suite of simple, composable command-line utilities as simple applications that use this library. Together with other commonly used machine-learning tools such as LibSVM and GraphViz, CompLearn forms an attractive offering in machine-learning frameworks and toolkits.
Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) is an open source library for machine learning licensed under the Mozilla Public License (MPL). We develop an open source machine learning toolkit which provides
The Maximum Entropy Toolkit provides a set of tools and library for constructing maximum entropy (maxent) model in either Python or C++.
Maxent Entropy Model is a general purpose machine learning framework that has proved to be highly expressive and powerful in statistical natural language processing, statistical physics, computer vision and many other fields.
Peach is a pure-python module, based on SciPy and NumPy to implement algorithms for computational intelligence and machine learning. Methods implemented include, but are not limited to, artificial neural networks, fuzzy logic, genetic algorithms, swarm intelligence and much more.
The aim of this library is primarily educational. Nonetheless, care was taken to make the methods implemented also very efficient.
Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features:
PyBrain is a modular Machine Learning Library for Python. It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.
PyBrain contains algorithms for neural networks, for reinforcement learning (and the combination of the two), for unsupervised learning, and evolution. Since most of the current problems deal with continuous state and action spaces, function approximators (like neural networks) must be used to cope with the large dimensionality. Our library is built around neural networks in the kernel and all of the training methods accept a neural network as the to-be-trained instance. This makes PyBrain a powerful tool for real-life tasks.
Not a library per-say, but a whole slew of example machine learning algorithms from the book "Machine Learning: An Algorithmic Perspective" by Stephen Marsland. All code is written in python.
Full applications that implement various machine learning or statistical systems oriented toward general learning (i.e., no spam filters and the like).
The dbacl project consist of a set of lightweight UNIX/POSIX utilities which can be used, either directly or in shell scripts, to classify text documents automatically, according to Bayesian statistical principles.
Vowpal Wabbit is a fast online learning algorithm. It features:
The core algorithm is specialist gradient descent (GD) on a loss function (several are available), The code should be easily usable.