Practical Course - Hands-on Deep Learning for Computer Vision and Biomedicine (IN0012, IN2106, IN4204)
Course 0000003678 in SS 2023
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 | In this course, we will develop deep learning algorithms for concrete applications in the fields of computer vision, biomedicine, and/or related fields. The main purpose of this course is to gain practical experience with deep learning, and to learn when, why and how to apply it to concrete, relevant problems. The topics will include: - Machine learning, deep learning - Standard and advanced neural network architectures - Tasks beyond supervised learning - Design of architectures, choice of loss functions, tuning of hyperparameters. The projects will be geared towards developing novel solutions for REAL OPEN PROBLEMS. Projects with different interesting problems and data representations will be offered. Regarding the APPLICATION PROCEDURE (important!), preliminary meeting, and introductory lectures, see the course webpage: https://vision.in.tum.de/teaching/ss2023/dlpractice |
|---|---|
| Links | TUMonline entry |