Manifold learning is an area of machine learning that seeks to identify

low-dimensional representations of high-dimensional data. In this talk I

will provide a geometric perspective on this area. One of the aims will

be to motivate the following talk, on the role that the differential

geometric connection can play in machine learning and shape recognition.