Master Practical Course - Machine Learning in Medical Imaging (IN2106, IN4142)
Lecturer (assistant) | |
---|---|
Number | 0000002094 |
Type | Practical course |
Duration | 6 SWS |
Term | Sommersemester 2023 |
Language of instruction | English |
Position within curricula | See TUMonline |
Dates | See TUMonline |
Dates
- 20.04.2023 16:00-18:00 03.13.010, Seminarraum
- 27.04.2023 16:00-18:00 03.13.010, Seminarraum
- 04.05.2023 16:00-18:00 03.13.010, Seminarraum
- 11.05.2023 16:00-18:00 03.13.010, Seminarraum
- 25.05.2023 16:00-18:00 03.13.010, Seminarraum
- 01.06.2023 16:00-18:00 03.13.010, Seminarraum
- 15.06.2023 16:00-18:00 03.13.010, Seminarraum
- 22.06.2023 16:00-18:00 03.13.010, Seminarraum
- 29.06.2023 16:00-18:00 03.13.010, Seminarraum
- 06.07.2023 16:00-18:00 03.13.010, Seminarraum
- 13.07.2023 16:00-18:00 03.13.010, Seminarraum
- 20.07.2023 16:00-18:00 03.13.010, Seminarraum
Admission information
See TUMonline
Note: Interested students should attend the preliminary meeting. This semester we will have a joint presentation of the CAMP MedIA courses offered, on Thursday, February 2nd, 2023, from 10:00 to 11:00 AM with the following agenda: Machine Learning in Medical Imaging (MLMI): 10:00 hrs. - 10:30 hrs. Deep Learning for Medical Applications (DLMA): 10:30 hrs. - 11:00 hrs. Feel free to attend all the talks of your interest. The sessions will be conducted in zoom: https://tum-conf.zoom.us/j/62071843483?pwd=elNRUGNJZmYreExKdUs5R2xaSUtRUT09
Note: Interested students should attend the preliminary meeting. This semester we will have a joint presentation of the CAMP MedIA courses offered, on Thursday, February 2nd, 2023, from 10:00 to 11:00 AM with the following agenda: Machine Learning in Medical Imaging (MLMI): 10:00 hrs. - 10:30 hrs. Deep Learning for Medical Applications (DLMA): 10:30 hrs. - 11:00 hrs. Feel free to attend all the talks of your interest. The sessions will be conducted in zoom: https://tum-conf.zoom.us/j/62071843483?pwd=elNRUGNJZmYreExKdUs5R2xaSUtRUT09
Description
This Master-Praktikum will consist in: (1) a few introductory lectures on machine learning and its application in different medical imaging applications, and (2), a machine learning project with a real medical application.