Universität Wien
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300052 SE Machine Learning in Genetics (2024S)

3.00 ECTS (2.00 SWS), SPL 30 - Biologie
Continuous assessment of course work
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. 12 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 05.03. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 19.03. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 09.04. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 16.04. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 23.04. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 30.04. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 07.05. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 14.05. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 21.05. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 28.05. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 04.06. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 11.06. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 18.06. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Tuesday 25.06. 11:30 - 13:00 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1

Information

Aims, contents and method of the course

• This course is designed to introduce the use of machine learning techniques in the field of population genetics. It focuses on applying these methods to analyze population genomic data.
• Participants will engage in practical problem-solving activities centered around key issues in population genetics, such as detecting introgression segments and identifying population structure. The course covers a range of machine learning paradigms including both supervised and unsupervised learning. Key models such as logistic regression, extra-trees classifiers, dimensionality reduction techniques, and artificial neural networks will be explored in detail.
• The course is structured to be highly interactive, allowing participants to apply their learning in real-time. Attendees can use their personal laptops, or the PCs provided in the lecture room for hands-on sessions.
• This course is designed for a broad audience; therefore, no prior programming experience is necessary.

Assessment and permitted materials

Two assessments are scheduled: one during session 6 and the other in session 12. These assessments will involve analysis tasks or presentations that are closely aligned with the material covered in preceding sessions.

Minimum requirements and assessment criteria

Successful completion and submission of the assigned analysis task (coding), and a scientific presentation are fundamental requirements for evaluation.
Regular attendance is mandatory to ensure a comprehensive understanding of the course material.

Examination topics

Submission of one assigned analysis task through Moodle.
Presentation of one scientific paper related to the topic (can be chosen from a list provided by the lecturers).

Reading list

• Bishop CM. 2006. Pattern Recognition and Machine Learning. Springer.
• Goodfellow I, Courville A, Bengio Y. 2016. Deep Learning. MIT Press.
• Huang X, Rymbekova A, Dolgova O, Lao O, Kuhlwilm M. 2024. Harnessing deep learning for population genetic inference. Nat Rev Genet 25: 61–78.

Association in the course directory

MAN W5, MAN 3

Last modified: We 31.07.2024 12:06