This course provides an introduction to stochastic optimal control and dynamic programming (DP), with a variety of engineering
applications. The course focuses on the DP principle of optimality, and its utility in deriving and approximating solutions to an ...
This hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insight ...
The course will introduce basic concepts for the description of electrons in condensed matter from a microscopic many-body approach,
and relate results obtained by quantum field theoretical methods to properties of the many-electron wave functions underly ...