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The 13th Machine Learning Summer School
The 13th Machine Learning Summer School was held in Cambridge, UK. This year's edition was organized by the University of Cambridge, Microsoft Research and PASCAL. The school offered an overview of basic and advanced topics in machine learning through theoretical and practical lectures given by leading researchers in the field. We hope to attract international students, young researchers and industry practitioners with a keen interest in machine learning and a strong mathematical background.
Cascading
Cascading is a feature rich API for defining and executing complex, scale-free, and fault tolerant data processing workflows on a Hadoop cluster.
The processing API lets the developer quickly assemble complex distributed processes without having to "think" in MapReduce. And to efficiently schedule them based on their dependencies and other available meta-data. Obviously simple data processing applications are supported as well, as complex jobs tend to start simple.
Introducing Apache Mahout
Once the exclusive domain of academics and corporations with large research budgets, intelligent applications that learn from data and user input are becoming more common. The need for machine-learning techniques like clustering, collaborative filtering, and categorization has never been greater, be it for finding commonalities among large groups of people or automatically tagging large volumes of Web content. The Apache Mahout project aims to make building intelligent applications easier and faster. Mahout co-founder Grant Ingersoll introduces the basic concepts of machine learning and then demonstrates how to use Mahout to cluster documents, make recommendations, and organize content.
Carrot2 - Open Source Search Results Clustering Engine
Carrot2 is an Open Source Search Results Clustering Engine. It can automatically organize small collections of documents, e.g. search results, into thematic categories.
YouTube - Machine Learning Course Playlist
This course (CS229) -- taught by Professor Andrew Ng -- provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.
Vowpal Wabbit (Fast Online Learning)
This is a project at Yahoo! Research to design a fast, scalable, useful learning algorithm.
MILEPOST GCC compiler
The overall objective of this project is to develop compiler technology that can automatically learn how to best optimise programs for re-configurable heterogeneous embedded processors. If successful we will be able to dramatically reduce the time to market of re-configurable systems. Rather than developing a specialised compiler by hand for each configuration, our project will produce optimising compilers automatically. Current handcrafted approaches to compiler development are no longer sustainable. With each generation of re-configurable architecture, the compiler development time increases and the performance improvement achieved decreases. As high performance embedded systems move from application specific ASICs to programmable heterogeneous processors, this problem is becoming critical
Machine Learning Open Source Software (mloss) | All entries
This site house a collection of software components which will be of interest to the inventive data miners.
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