| Deep Vision [2021 SoSe] | ||
|---|---|---|
| Code IDV |
Name Deep Vision |
|
| LP 6 LP |
Dauer one semester |
Angebotsturnus irregular |
| Format lecture 2 SWS, tutorial 2 SWS |
Arbeitsaufwand 180h; thereof 60h lectures and tutorials 70h lecture wrap-up, programming excercises, and homework 50h preparation for project work, examination |
Verwendbarkeit B.Sc. Angewandte Informatik, M.Sc. Angewandte Informatik, M.Sc. Scientific Computing |
| Sprache |
Lehrende |
Prüfungsschema |
| Lernziele | To have reached an understanding of the foundations underlying deep learning To be able to practically apply deep learning algorithms to new problems in Computer Vision To have a firm command of the algorithmic basics and to be able to analyze and solve new problems in novel application areas To be capable of understanding and analyzing the latest publications in deep learning and Computer Vision and to evaluate their strengths and weaknesses. To have reached understanding of state-of-the-art deep learning algorithms for Computer Vision with the ability to relate and contrast different concepts. |
|
| Lerninhalte | Methods in Computer Vision based on Deep Learning, esp.: • deep learning in Computer Vision and convolutional neural networks • generative/discriminative methods • supervised/unsupervised methods, reinforcement learning • video analysis • Object and action recognition • local and global feature extraction • hierarchical object representations • novel applications |
|
| Teilnahme- voraus- setzungen |
recommended are: solid programming skills and knowledge of basic calculus, statistics, and linear algebra | |
| Vergabe der LP und Modulendnote | Bestehen der Modulprüfung | |
| Nützliche Literatur | Will be presented in class | |