Course description
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 the familiar case of particle tracking, to more complex tasks in image segmentation and feature recognition, machine-learning (ML) methods are rapidly taking the scene. This course, aimed at doctoral students, has the goal to guide attendees through a progression from basic 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. 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. As a prerequisite, basic programming knowledge in Python is required.
Requirements and Selection
Entry requirements
Basic Python knowledge
Selection
Priority will be given to students that have the necessary background and can benefit most of this course in their research.
Course syllabus
NFFY314
Department
Department of Physics
Subject
Natural Science and Mathematics
Type of course
Method course
Keywords
Machine learning, image analysis