During summer school, we will discuss quite advanced topics of Bayesian Deep Learning. The school lasts only 6 days, and we do not have enough time for a detailed introduction into basic Bayesian Methods and Deep Learning. That's why we expect you to come with the knowledge of some basic concepts.
Below we present a checklist of concepts which you have to be familiar with before the summer school. We also provide links to the materials on these topics. We recommend studying the materials on concepts you are not familiar with. Otherwise, it will be hard for you to follow the school lectures.
Although there are a few intro lectures on Bayesian approach in the program, they are not enough to understand the specifics of this approach if it is your first acquaintance. But they will be helpful to understand the connections between methods if you studied the materials in advance.
We also recommend taking a look at a the Matrix Cookbook. It is a good collection of useful mathematical facts that will be useful at the summer school. You should understand basic concepts from this book (basic operations with matrices, matrix derivatives, properties of distributions etc).
There are two types of classes at the summer school: lectures and practical sessions. For the practical sessions, please bring your laptop with python 3.7, pytorch (version 1.1) and torchvision installed. For python 3.7, we recommend installing an Anaconda distribution.
Also, please download a MNIST dataset before the school. We recommend running this script to test your installation, and the script will also download MNIST using torchvision wrapper (in the same manner as we will load data at the school).
School materials (lectures and assignments) will be available in the GitHub repository.
The School will be held at the InLiberty space at Stolyarnyy Pereulok, 3/1, Moscow near the metro station "Ulitsa 1905 goda" (Purple line)