Workload:
180 h
|
Module Number ML-4511 |
Module Title Machine Learning in Gaphics, Vision, and Language |
Lecture Type(s) Practical Course |
|---|---|---|
| ECTS | 6 | |
|
Work load - Contact time - Self study |
Workload:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
| Duration | 1 Semester | |
| Frequency | In the summer semester | |
| Language of instruction | English | |
| Type of Exam | Project, presentation, and written elaboration |
|
| Content | Implementation of advanced applications and programs in the intersection of machine learning in computer graphics / computer vision / natural language processing |
|
| Objectives | Students will know how to efficiently implement current machine learning approaches in the areas of segmentation, 3D reconstruction, scene analysis, rendering, interaction, or language processing. They will be able to independently plan and execute programming projects in groups using neural networks, transformers or other ML approaches for data acquisition, reconstruction and representation as well as for natural language interaction or explanation. |
|
| Allocation of credits / grading |
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%) |
|
| Prerequisite for participation | There are no specific prerequisites. | |
| Lecturer / Other | Lensch | |
| Literature | Teilnahmevoraussetzungen: Deep Learning, von Vorteil sind Graphische Datenverarbeitung oder Computer Vision ---- Course prerequisites: Completion of Deep Learning; previous completion of Computer Graphics or Computer Vision is advantageous |
|
| Last offered | unknown | |
| Planned for | Sommersemester 2024 | |
| Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-PRAX, MEDI-VIS, ML-CS, ML-DIV | |