**Zohar Ringel** – The Hebrew University of Jerusalem, Israel

Topics:New results in ML: Mapping over-parameterized fixed-depth deep neural networks to Gaussian Processes. The Neural Tangent Kernel. Field theory description of over-parametrized networks. The information bottleneck. Identifying slow degrees of freedom, learning Exact Holomorphic Mappings in ADS/CFT.

**Lei Wang** – Institute of Physics, Chinese Academy of Sciences, China

Topics:Deep learning theory, Generative models for physicists, Differentiable programming, Representation Learning. Examples to be covered: computation graph, automatic differentiation, variational inference, inverse Hamiltonian design. Applications of deep learning to statistical and quantum many-body physics.

**Roger Melko** – Perimeter Institute and the University of Waterloo, Canada

Topics:Lattice models for statistical physics, Monte Carlo methods, supervised and unsupervised learning, neural networks, Boltzmann machines, and deep learning.

**Eun-Ah Kim** – Cornell University, USA

Topics:Lattice models for statistical physics, Monte Carlo methods, supervised and unsupervised learning, neural networks, Boltzmann machines, and deep learning.