Many machine learning problems can be cast as a convex
optimization problem. Among them are least squares regression, SVMs,
kernel learning, metric embeddings, etc. In the talk I will provide
a small general overview and I will present a project on how to
obtain fast solvers for convex optimization problems from
simple mathematical descriptions. The automatically generated code
can be orders of magnitude faster than comparable approaches.