The Deep Learning for NLP course covers advanced topics in deep learning architectures for natural language processing. The focus is on attention-based architectures (like Transformers), structure processing and variational-Bayesian approaches, and why these models are particularly suited to the properties of human language, such as categorical, unbounded, and structured representations.
- Professor: James HENDERSON
- Professor: James Henderson
- Teacher: Andrei Catalin Coman
- Teacher: Fabio James Fehr
- Teacher: Haruki Shirakami
This course describes theory and methods for decision making under uncertainty under partial feedback.
- Professor: Volkan Cevher
- Teacher: Elias Abad Rocamora
- Teacher: Pedro Abranches De Carvalho
- Teacher: Leello Tadesse Dadi
- Teacher: Thomas Michaelsen Pethick
- Teacher: Mehmet Fatih Sahin
- Teacher: Luca Viano
- Professor: Michaël Unser
- Teacher: Philippe Thévenaz
