Credits: 5

Schedule: 26.02.2019 - 31.05.2019

Teaching Period (valid 01.08.2018-31.07.2020): 

IV-V (Spring)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

Understanding of the general principles of deep learning, and the central deep learning methods discussed in the course. After the course, you should be able to apply them to real-world data sets.

Content (valid 01.08.2018-31.07.2020): 

Fundamental and current topics of deep learning. Implementing algorithms on a computer are a part of the course and the programming language is Python. Python-based softwares that allow for symbolic differentiation will also be used.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Lectures, exercises, project work, and an examination.

Workload (valid 01.08.2018-31.07.2020): 

1 2 lectures per week (1h 30 min total each), one (primarily computer) exercise session per week (1 h 30 min total each), and the rest for studying the course material, doing exercises, the mini-project, and the examination.

Study Material (valid 01.08.2018-31.07.2020): 

Material produced for the course such as lecture slides, external material. The external material will include the 'Deep Learning'-book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, published by MIT Press in 2016 (online version freely available at, Python material.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

CS-E4810 Machine Learning and Neural Networks.

Prerequisites (valid 01.08.2018-31.07.2020): 

CS-E3210 Machine Learning: Basic Principles'-course or knowledge, skills and experience equivalent to that obtained from completing the course; basic courses in mathematics and probability; Python programming basics.

Grading Scale (valid 01.08.2018-31.07.2020): 



Registration and further information