Machine Learning for Computer Vision (IN2357)
Course 0000003346 in WS 2020/1
General Data
Course Type | lecture with integrated exercises |
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Semester Weekly Hours | 4 SWS |
Organisational Unit | Informatics 9 - Chair of Computer Vision and Artificial Intelligence (Prof. Cremers) |
Lecturers |
Lukas Köstler Rudolph Triebel Responsible/Coordination: Daniel Cremers |
Dates |
Fri, 16:00–18:00, virtuell Wed, 16:00–18:00, virtuell Thu, 16:00–18:00, virtuell |
Assignment to Modules
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IN2357: Maschinelles Lernen für Computersehen / Machine Learning for Computer Vision
This module is included in the following catalogs:- Catalogue of non-physics elective courses
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 | Machine Learning methods are an essential component for the solution of important problems in computer vision, including object classification and pose estimation, object tracking, image segmentation, denoising of images, or camera calibration. Therefore, in this lecture the most relevant methods of Machine Learning are presented and derived mathematically. These mainly comprise: - kernel methods, specifically Gaussian processes - metric learning - clustering such as GMMs or spectral clustering - boosting and bagging - hidden Markov models - neural networks and deep learning * - sampling methods, specifically MCMC The focus here is laid on a broad understanding of these methods rather than in a deep specification of single approaches. Practical experience is acquired by means of programming tasks. *The topic “deep learning” will be handled only marginally. For a broader treatment of this topic, we refer to other classes, e.g. IN2346. |
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Links | TUMonline entry |
Equivalent Courses (e. g. in other semesters)
Semester | Title | Lecturers | Dates |
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SS 2022 | Machine Learning for Computer Vision (IN2357) | Triebel, R. |
Fri, 12:00–14:00, Interims I 102 Thu, 16:00–18:00, Interims I 102 |
SS 2021 | Machine Learning for Computer Vision (IN2357) |
Demmel, N.
Yenamandra, T.
Responsible/Coordination: Cremers, D. Assistants: Triebel, R. |
Wed, 16:00–18:00, virtuell Fri, 14:00–16:00, virtuell and singular or moved dates |
SS 2020 | Machine Learning for Computer Vision (IN2357) |
Sommer, C.
Responsible/Coordination: Cremers, D. Assistants: Triebel, R. |
Thu, 16:00–18:00, virtuell Fri, 12:00–14:00, virtuell |
WS 2019/20 | Machine Learning for Computer Vision (IN2357) |
Demmel, N.
Responsible/Coordination: Cremers, D. Assistants: Triebel, R. |
Fri, 16:00–18:00, Interims I 102 Wed, 16:00–18:00, PH HS2 Thu, 16:00–18:00, PH HS1 |
SS 2019 | Machine Learning for Computer Vision (IN2357) |
Häfner, B.
Responsible/Coordination: Cremers, D. Assistants: Triebel, R. |
|
WS 2018/9 | Machine Learning for Computer Vision (IN2357) |
Responsible/Coordination: Cremers, D. Assistants: Triebel, R. |
|
SS 2018 | Machine Learning for Computer Vision (IN2357) |
Schubert, D.
Responsible/Coordination: Cremers, D. Assistants: Triebel, R. |