Abstract
This seminar will be held in 210 Hodson Hall from 11 am to 12 pm. Refreshments available at 10:45am. The problem of learning is one of the main gateways to making intelligent machines and to understanding how the brain works. In this talk I will give a brief overview of our recent work on learning theory, including new results on predictivity and stability of the solution of the learning problem. I will then describe recent efforts in developing machines that learn in applications such as visual recognition and computer graphics. In particular, I will summarize our work on trainable, hierarchical classifiers for problems in object recognition and especially for face and person detection. I will also describe how we used the same learning techniques to synthesize a photorealistic animation of a talking human face. Finally, I will speculate briefly on the implication of our research on how visual cortex learns to recognize and perceive objects. Relevant papers can be downloaded from http://www.ai.mit.edu/projects/cbcl/publications/all-year.html.

Biography
Tomaso A. Poggio is the Eugene McDermott Professor at the Department of Brain and Cognitive Sciences at MIT; he is director of the Center for Biological and Computational Learning; member of the Artificial Intelligence Laboratory and of the McGovern Institute for Brain Research. His work is motivated by the belief that the problem of learning is the gateway to making intelligent machines and understanding how the brain works. Research on learning in his group follows three directions: mathematics of learning theory and ill-posed problems, engineering applications (in computer vision, computer graphics, bioinformatics, datamining and artificial markets) and neuroscience of learning, presently focused on how visual cortex learns to recognize and represent objects.