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301169 SE Applied Machine Learning for biological problems (2024W)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 12.09.2024 14:00 to Th 26.09.2024 18:00
- Deregistration possible until Th 26.09.2024 18:00
Details
max. 20 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
The classes are all planned to be in person. In the event of deviations, classes can also be held online. For online attendance, a Zoom account is required.
- Thursday 03.10. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 10.10. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 17.10. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 24.10. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 31.10. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 07.11. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 14.11. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 21.11. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 28.11. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 05.12. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 12.12. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 09.01. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 16.01. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 23.01. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
- Thursday 30.01. 15:00 - 18:15 Seminarraum 1.5, Biologie Djerassiplatz 1, 1.012, Ebene 1
Information
Aims, contents and method of the course
In this course, we focus on methods from Data Science and their application in biology. Participants will learn basic concepts of machine learning, including an introduction to the Python data science stack, as well as several specific methods and evaluation strategies. The lectures are supplemented by practical examples and discussions on current literature in the field of applied machine learning for biological problems. After the course, participants will be able to decide, whether a given biological problem can be tackled with machine learning. Further, participants will be able to assess the quality of machine learning approaches in the scientific literature.The first half of each unit (15:00-16:30) will consist of lectures and joint work on the teaching material. The second half of each unit (16:45-18:15) will be kept free for supervision matters.
Assessment and permitted materials
Continuous assessment during the course by presentations of literature and presentation of own results from practical experiments.
Minimum requirements and assessment criteria
A standard PC/laptop is required (any OS, Linux preferred). We can provide a few laptops if needed.
Options for high-performance computing (cluster or cloud services) will be provided in the course.
There are no strict course prerequisites, but basic command line and Python skills are recommended.
While the seminar will start with an introduction to Python, some basic knowledge beforehand is beneficial.
Beginners are encouraged to self-study before the course starts.Students can earn a total of 100 points for the areas of active participation (24 points), homework assignments (46 points), and presentations given (30 points).Grading:
1: 100-90 points
2: 89-80 points
3: 79-65 points
4: 64-50 points
5: 0-50 points
Options for high-performance computing (cluster or cloud services) will be provided in the course.
There are no strict course prerequisites, but basic command line and Python skills are recommended.
While the seminar will start with an introduction to Python, some basic knowledge beforehand is beneficial.
Beginners are encouraged to self-study before the course starts.Students can earn a total of 100 points for the areas of active participation (24 points), homework assignments (46 points), and presentations given (30 points).Grading:
1: 100-90 points
2: 89-80 points
3: 79-65 points
4: 64-50 points
5: 0-50 points
Examination topics
Understanding of methods, practical experiments, analysis and interpretation of results and scientific literature.
Reading list
Literature and further materials will be provided during the course.
Association in the course directory
MMEI II-1.2, MMEI II-2.2, MMB IV., MNEU V.
Last modified: We 14.08.2024 14:06