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Projects at the intersection of neuroscience and machine learning

8.3593

Dozenten

Beschreibung

Content and goal:
In this course, you will work on your own in-depth project, either alone or (preferably) with at least one other student. The projects can be chosen from a list provided by the instructors or decided jointly with students and instructors. The course will begin with in-depth discussions to help you decide the details of the projects. In the subsequent weekly meetings, you will provide brief summaries of your progress - what is done, what are the roadblocks, and what are the next steps, and the group and instructors will provide guidance on how to proceed further. Students will be required to review each-other’s code to learn to write clearly/accessibly, and to take the perspective of an external code-reviewer. By the end you will have completed a project at the intersection of neuroscience and machine learning, you will have learned to review code, to communicate problems, and you will have gotten an in-depth insight into the research field as such. You will be required to document your project in the form of a research paper or a thesis.

Target audience:
Master students who have at least completed an introductory course in cognitive neuroscience or neurobiology and an introductory course in deep learning. Students are also required to be proficient in python.

Number of participants:
The number of participants of this course is limited to ensure that we can offer the best in-depth project guidance and support. If more students than the maximum number apply, we will choose a subgroup based on fulfilment of course criteria (see "target audience"), as well as past experience and, likely, motivation letters. The exact procedure will be communicated in the first session once we can tell how many students exactly are actively parttaking.

Grading:
If you participate with a *standalone* project your grade will be based on your project, documentation and your active participation in the weekly meetings. If you take this seminar for your thesis, no credit/grade can be given.

Weitere Angaben

Ort: 50/E04
Zeiten: Di. 13:00 - 17:00 (wöchentlich)
Erster Termin: Dienstag, 11.04.2023 13:00 - 17:00, Ort: 50/E04
Veranstaltungsart: Seminar (Offizielle Lehrveranstaltungen)