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Many of you will have played with cellular automata such as Conway’s Game of Life. These are model systems in which complex and chaotic behaviors emerge from simple dynamical rules. Motivated by quantum computation, physicists have in recent years begun to study quantum circuits, which are in some way quantum analogues of cellular automata. In this talk I’ll discuss some of the similarities and differences between these two classes of systems, and what they can teach us about classical and quantum dynamics more generally.

I’ve been doing some research about the “best” way to make online demos. Obviously that’s pretty subjective, but I think a pretty reasonable goal is not having to completely reimplement code I may have written and used in the course of doing my research.

In Lecture 1 we introduced the idea of Variational Inference (VI), which turns the problem of inference in latent variable models into one of optimization. That was a rather high-level view: in this lecture we are going to see in more detail how this works in practice in modern approaches that leverage neural networks and automatic differentiation.

In these lectures we are going to explore some connections between machine learning (ML) and (classical) statistical mechanics (SM). To be precise, we are going to see how the appearance of probabilistic models with large numbers of variables in both fields means that certain theoretical concepts and tools can be applied in both.

For the new academic year I’m switching over to a new site based on the amazing Academic theme for Hugo by George Cushen. Support for LaTeX, reveal.js slides (which I’ve used for my talks for a few years now) and even Jupyter notebooks is all there out of the box.