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*** Note 1. The course will run through both P1 and P2. P1 will include lectures, writing assignments, and project proposal (3HP) and P2 will have the students conduct their final project (3HP) ***
*** Note 2. The course will be held onsite.
*** Note 3. The course has limited seats (100-120 students). The applicants will be admitted according to the following priority groups ***
Applicants are admitted according to the priority group they fall into. If the applicants of the a group surpasses the number of available seats, the applicants will be sorted by their grade in the DL/ML courses they have completed and the top applicants are admitted accordingly.
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 basic 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/NeurIPS/ICLR/JMLR/CVPR/ECCV/ICCV/EMNLP/ACL. This would mean that not having the proper background can make understanding and passing the course difficult. The advanced topics we will cover are versatile but at the high level includes probabilistic deep networks, uncertainty estimation, deep generative models, deep learning with limited data, and understanding deep networks.
The course will also involve reading, commenting and possibly presenting papers from the recent publications as individual assignments. The final form of assessment is an implementation project which is done in a group.