Predicting the type of cancer from DNA chips. Learning from example. (Image courtesy of Poggio Laboratory, MIT Department of Brain and Cognitive Sciences.)
During this course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. Supervised learning with the use of regression and classification networks with sparse data sets will be explored. The extensive reading list
grounds the future researcher in the field of learning networks. Lecture notes
provide an overview of each topic covered in the class.
The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.
*Some translations represent previous versions of courses.