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.
- Professor: James Henderson
- Teacher: Jan Frederik Jonas Florian Mai
- Teacher: Alireza Mohammadshahi
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.
- Teacher: Guglielmo Frigo
- Teacher: Mario Paolone
- Professor: Michaël Unser
- Teacher: Julien René Fageot
- Teacher: Philippe Thévenaz
This course describes theory and methods for decision making under uncertainty under partial feedback.
- Professor: Volkan Cevher
- Teacher: Igor Krawczuk
- Teacher: Kamalaruban Parameswaran
- Teacher: Paul Thierry Yves Rolland
- Teacher: Cheng Shi