Workload:
90 h
|
Module Number MEDZ-4523 |
Module Title Machine Learning to Fight Infections |
Lecture Type(s) Seminar |
|---|---|---|
| ECTS | 3 | |
|
Work load - Contact time - Self study |
Workload:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
| Duration | 1 Semester | |
| Frequency | Irregular | |
| Language of instruction | English | |
| Type of Exam | Oral presentation and written report |
|
| Content | Machine learning is used in many areas of medicine to automate certain processes, find intelligent representations of complex data, and make predictions about phenotypes of interest or other labels. Machine learning techniques have also been applied and developed in infection research for quite some time. In this seminar, we will cover several areas ranging from Machine Learning assisted Computational Epidemiology, to Resistance Prediction of Infectious Agents, to Predicting Viral Evolution. |
|
| Objectives | The students know and can critically reflect the most important concepts, theories and methods in how to control infections with machine learning metho |
|
| 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 | Pfeifer | |
| Literature | - |
|
| Last offered | unknown | |
| Planned for | Sommersemester 2024 | |
| Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS | |