Upon completion of the course, students will be able to:
- apply neural networks to real-world problem-solving
- conduct comparative analysis, both theoretical and empirical, in order to determine which neural network is most suitable for a particular task
- design different kinds of neural networks, evaluate their performance, and use them to solve complex problems
- apply deep learning to solve real-world problems
- show understanding of and be able to use software tools for modern deep learning
- explain both the advantages and the limitations of solutions based on neural networks and deep learning
Project work that is submitted in written and oral form 3 Credits
Labs 3 Credits
Seminars and written reflections 1.5 Credits
Forms of Study
Lectures, compulsory labs, projectwork, and seminars.
Project work U-VG
Labs, seminars, and written reflections U-G
The final grade of the course is determined based on the case study and the project work.
- 30 credits second level within the Mainfield of Microdata Analysis