Practical Course - Creation of Deep Learning Methods (IN0012, IN2106, IN4292)
Course 0000003099 in WS 2020/1
General Data
| Course Type | practical training |
|---|---|
| Semester Weekly Hours | 6 SWS |
| Organisational Unit | Informatics 9 - Chair of Computer Vision and Artificial Intelligence (Prof. Cremers) |
| Lecturers |
Vladimir Golkov Responsible/Coordination: Daniel Cremers |
| Dates |
Assignment to Modules
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IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | Using deep learning to solve real problems often requires the creation of novel appropriate deep learning methods, rather than just out-of-the-box usage of existing architectures. In this practical course, students will choose REAL OPEN PROBLEMS and learn how to analyze them, how to identify the requirements that a deep learning method should fulfill, and how to create novel deep learning methods that fulfill these requirements. Some of the projects that can be chosen also include the analysis of design principles of existing methods, and subsequent usage of these design principles to create new methods. Regarding the preliminary meeting and content of lectures, see the course website: https://vision.in.tum.de/teaching/ws2020/create_dl |
|---|---|
| Links |
Additional information TUMonline entry |