Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
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.
Schedule: 26.02.2019 - 31.05.2019
Teacher in charge (valid 01.08.2020-31.07.2022): Alexander Ilin
Teacher in charge (applies in this implementation): Alexander Ilin
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
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
Exercises and exam.
1 lecture per week (1h 30 min total each), one computer exercise session per week (1 h 30 min total each), and the rest for studying the course material and doing exercises.
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
CS-E4810 Machine Learning and Neural Networks.
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.