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The Java Data Mining Package (JDMP) is an open source Java library for data analysis and machine learning. It facilitates the access to data sources and machine learning algorithms (e.g. clustering, regression, classification, graphical models, optimization) and provides visualization modules. It includes a matrix library for storing and processing any kind of data, with the ability to handle very large matrices even when they do not fit into memory. Import and export interfaces are provided for JDBC data bases, TXT, CSV, Excel, Matlab, Latex, MTX, HTML, WAV, BMP and other file formats. JDMP provides a number of algorithms and tools, but also interfaces to other machine learning and data mining packages (Weka, LibSVM, Mallet, Lucene, Octave).

Data mining and visualisation tool that connects to a number of data sources (including Matlab and Weka)

datamining

LGPL 3

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Stephen Marsland, Massey University

"I've written a textbook ... there are lots of Python code examples in the book, and the code is available here."

Machine Learning: An Algorithmic Perspective

"I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence. There are lots of Python code examples in the book, and the code is available here. Where special datasets are used they are provided with the code, and there are links to additional datasets at the bottom of the page."

I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence.

I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence. There are lots of Python code examples in the book, and the code is available here.

Machine Learning: An Algorithmic Perspective

Python codes from a textbook entitled "Machine Learning: An Algorithmic Perspective"

by Stephen Marsland

I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence. There are lots of Python code examples in the book, and the code is available here. Where special datasets are used they are provided with the code, and there are links to additional datasets at the bottom of the page.

Interesting comparative performance of various algorithms on different data

A highly informative visualization of the biases of different ML classifiers. Really useful, especially for talks to non-experts.

Efficient Learning, Large-scale Inference, and Optimisation Toolkit

Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) is an open source library for machine learning licensed under the Mozilla Public License

Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) is an open source library for machine learning licensed under the Mozilla Public License (MPL).