This short course will focus on modern, data driven analytical methods in the field of Deep Learning with MATLAB. Deep Learning represents powerful artificial intelligence tools used to solve complex modeling problems in earth and ocean sciences, planetary and atmospheric sciences, and related math and geoscience fields. The MATLAB based Deep Learning platform provides algorithms and tools for creating and training deep neural networks. These networks are used to simulate processes of past, present and future environmental events in this wide range of disciplines.

Participants will be able to adopt concepts of Deep Learning for their areas of research such as dynamics, preconditions, and trends related to the surface, subsurface and the atmosphere of the planets. The content level will be 80% beginner, 10% intermediate, and 10% advanced. Scientists from all disciplines are invited to participate in this course. Any previous experience with Deep Learning and distributed computing will be beneficial but not necessary for participation.

The maximum number of participants is 65, in order to guarantee direct supervision for the hands-on part of the session.