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, 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.