Universität Wien
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301169 SE Applied Machine Learning for biological problems (2023W)

5.00 ECTS (3.00 SWS), SPL 30 - Biologie
Continuous assessment of course work
ON-SITE
Moodle

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).

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 05.10. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 12.10. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 19.10. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 09.11. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 16.11. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 23.11. 13:15 - 14:45 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 30.11. 13:15 - 14:45 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 07.12. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 14.12. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 11.01. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 18.01. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Thursday 25.01. 11:30 - 13:00 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, 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.

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.

The course will be evaluated on the basis of active participation (30%), assignments (40%), and presentation of literature/results (30%).

Grading:
100-90%: 1
89-80%: 2
79-65%: 3
64-50%: 4
<50%: 5

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: Tu 12.09.2023 17:28