Machine learning reductions are about reusing solutions to simple, core problems in order to solve more complex problems. A basic difficulty in applying machine learning in practice is that we often need to solve problems that don't quite match the problems solved by standard machine learning algorithms. Reductions are techniques that transform such practical problems into core machine learning problems. These can then be solved using any existing learning algorithm whose solution can, in turn, be used to solve the original problem. The material that we plan to cover is both algorithmic and analytic. We will discuss existing and new algorithms, along with the methodology for analyzing and creating new reductions. We will also discuss common design flaws in folklore reductions. In our experience, this approach is an effective tool for designing empirically successful, automated solutions to learning problems.