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Machine Learning (MaP Doctoral School)
Last Updated: 2026-02-05 16:02:03
Abstract
Microscopy images, irrespective of the specific imaging technique, e.g. optical, electron or atomic force microscopy, are an extremely rich source of quantitative data. With the ever increasing push to enhance spatial and temporal resolution, as well as with the increase of storage and computing power, very large amounts of data are easily generated and require automation for data extraction. From
Objective
This course, aimed at doctoral students, has the goal to guide attendees through a progression from basic machine learning (ML) methods, through the extension of those to increasingly complex analyses all the way to offering the students the possibility to directly apply the concepts learned during the course to their own data.
Content
The course will combine lectures with hands-on exercises in concentrated blocks across the semester. Students have the possibility to select different blocks, for instance if they already have basic ML programming knowledge. The students will also be able to work on a project related to their research where they apply ML to some imaging data.
General Information
- Language
- English
- Levels
- DR
- Frequency
- Yearly recurring
Examination
- Type
- ungraded semester performance
Registration & Places
- Signup End
- 14.09.2022
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| practical/laboratory course |
Machine Learning (MaP Doctoral School)
Block course 3x 3 days:
October 5-7, 2022
November 2-4, 2022
December 14-16, 2022
Plus one day of presentation (end of February 2023)
|
|
180 h semesterly |
Offered In
-
Doctorate Materials Science (Further information at: )
-