MATHEMATICAL BIOLOGY SEMINAR

        Many algorithms in machine learning involve changing the underlying dimensionality of the data set.  Unsupervised learning techniques such as principal components analysis typically involve dimensionality reduction, whereas supervised learning techniques such as support vector machines can be understood as mapping the data to a higher dimensional space.  After reviewing recent machine learning algorithms that utilize changes in dimensionality, I will show how equivalent problems emerge in artificial sensorimotor systems.  Sensory processing typically involves mapping high-dimensional sensory inputs onto a smaller number of perceptually-relevant features, whereas motor learning involves driving a large number of actuator parameters with a smaller number of control variables.  I will illustrate how dimensionality plays an important role in sensorimotor learning with demonstrations on some prototypical robotic systems.