*** Note. Due to the situation caused by the spread of covid-19 the offering of the course in 2020 P1 is currently planned to be fully online. ***
As the topic suggests the course will delve into some advanced topics of deep learning. As such it requires solid background in core knowledge of deep networks (e.g. DD2424), machine learning in general, and math (calculus, probability theory and algebra). During the course we will go through recent advances in the field of deep learning which essentially means seminal papers from recent (~2014-2019) top venues such as ICML/NIPS/ICLR/JMLR/CVPR/ECCV/ICCV/EMNLP/ACL. This would mean that not having the proper background can make understanding and passing the course quite hard. The advanced topics we will cover are still tentative but includes probabilistic deep networks, uncertainty estimation, deep generative models, meta-learning, understanding deep networks, and the like.
The course will also involve reading, commenting and possibly presenting papers from the recent publications as individual assignments. The final form of assessment will be either writing a full review essay on the advanced topics or a an implementation project which more likely will be done in groups. The tentativeness of the assignment type and final evaluation is due to the unknown number of students who will register for this first round of the course and will be announced in the first lecture in August.