Schedule: 26.02.2019 - 31.05.2019
Teaching Period (valid 01.08.2018-31.07.2020):
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 http://www.deeplearningbook.org), 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):