Deep Learning

The topics of the course include feed forward networks, back-propagation, optimization, convolutional neural networks, sequence modelling, recurrent neural networks.

The final grade is based on the exam (3h - week 2.6) and a single final assignment (due in week 2.6). Other assignments during the course are formative and meant to prepare the student for the final assignment.

Final grade: 0.4 * Final assignment + 0.6 * Exam

In order to pass, the student needs to obtain at least 5.8 as final grade and at least 5.0 for both the final assignment and the exam. Students with a final assignment grade between 4.0 and 5.7 have the possibility to work at a repair final assignment with the limitation of a max grade of a 6. There is a resit opportunity for the exam.

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 29, sub 4)