The program focused on the following four themes: - Optimization: How and why can deep models be fit to observed (training) data? - Generalization: Why do these trained models work well on similar but unobserved (test) data? - Robustness: How can we analyze and improve the performance of these models when applied outside their intended conditions? - Generative methods: How can deep learning be used to model probability distributions?