In addition to the challenges we face in acquiring, storing, and transmitting very large amounts of data, we also frequently desire to "learn" from the data in a number of senses. This arises in many important and emerging signal processing problems when we lack a priori analytical models. In this case we must learn data models and tune processing algorithms based entirely on training data. Example applications include search engines, medical diagnosis, detecting credit card fraud, stock market analysis, speech and handwriting recognition, object recognition in computer vision, and spam filtering. We are exploring a wide range of machine learning algorithms to that aid in tasks including data visualization and exploration, dimensionality reduction, nonlinear regression, and pattern classification.