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Practical Course - Expert-Level Deep Learning for Computer Vision and Biomedicine (IN0012, IN2106, IN4204)

Course 0000003678 in WS 2019/20

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
Thomas Möllenhoff
Responsible/Coordination: Daniel Cremers
Dates Tue, 14:00–16:00, MI 03.13.010

Assignment to Modules

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 UPDATED DESCRIPTION! PLEASE READ THE ENTIRE COURSE DESCRIPTION AND SEND AN EMAIL WHEN APPLYING. In this course, we will develop deep learning algorithms for concrete applications in the field of computer vision and biomedicine. 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: - Basics of machine learning and deep learning - Standard and advanced 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.
Links Additional information
TUMonline entry
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