The Deep Learning for Natural Language Processing course provides an overview of neural network based methods applied to text.  The focus is on models particularly suited to the properties of human language, such as categorical, unbounded, and structured representations, and very large input and output vocabularies.

This course introduces the principles of model identification for non-linear dynamic systems, and provides a set of possible solution methods that are thoroughly characterized in terms of modelling assumptions and uncertainty levels. The course is intended to provide the mathematical tools to model, identify and predict the state and behaviour of complex systems, based on a set of experimental measurements and observations.

This course describes theory and methods for decision making under uncertainty under partial feedback.

Block on course on optimal control. Taught by Prof. Timm Faulwasser