'Still, Mr. Holt scored a minor triumph for the often-castigated political class. “I think more of Congress just hearing about it,” said Tom M. Mitchell, a computer scientist at Carnegie Mellon University and an artificial intelligence expert.'

Fabulous piece by @josephreisinger about tradeoffs between data and algorithms in machine learning via @ginablaber

hmason

PyBrain is a modular Machine Learning Library for Python. Its 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.
...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.

"Apache Mahout's goal is to build scalable machine learning libraries. "

The Prediction API enables access to Google's machine learning algorithms to analyze your historic data and predict likely future outcomes. Upload your data to Google Storage for Developers, then use the Prediction API to make real-time decisions in your applications. The Prediction API implements supervised learning algorithms as a RESTful web service to let you leverage patterns in your data, providing more relevant information to your users. Run your predictions on Google's infrastructure and scale effortlessly as your data grows in size and complexity.

pdf available online

Rankings are based on the following question: Which blogs should one read to be most up to date, i.e., to quickly know about important stories that propagate over the blogosphere?

svd and congressional records

using svd to cluster blog political linking

The part I found interesting was the first detailed description of using the MapReduce model to run large-scale Expectation Maximization (EM) computations in parallel. An implementation of this on Hadoop and Amazon EC2 will let you tackle some large scale