Summer Term 2020

Please note that only 'active' doctoral students of the GTC can participate in the courses listed.  Doctoral students who have not yet passed their admission interviews ('applicants') and guest students from other faculties can participate only in case of vacancies.

Courses indicated as 'Elective' will run throughout the semester and take place once a week. They are specialist courses offered to masters students, however, they might also be of interest to doctoral students working or planning to work in that particular field.

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Course title

Theoretical Methods for Computational Neuroscience II (Elective)

Lecturer
Giese
Credits
1.5
Course content / topics

Objective:
Computational neuroscience builds partly on advanced theoretical concepts from multiple fields, including mathematics, physics, control theory, machine learning, and signal processing. The course consists of a series of tutorials on such advanced topics combined with in-class exercises. The course runs over two semesters with about 7 sessions per semester (credits: 1.5 per semester). Topics complement the material covered in the main lectures Neural Dynamics and Machine Learning.

Course Schedule & Topics

Learning targets:
Participants will learn advanced mathematical methods with relevance for machine learning, including specifically convex and non-convex optimization. In addition, some advanced methods from machine learning will be covered, such as variational inference, the analysis of causality, and basics of deep neural networks.
Prerequisites
The course complements specifically the Lectures Machine Learning I +II. Some basic concepts cannot be treated in sufficient depth in these courses. Thus, the benefit will be optimal for students who also attend these other courses.

Specific literature will be provided during the lecture.

Comment: Theoretical Methods for Computational Neuroscience – I and II can be taken independently, each with 1.5 credits.

ILIAS Online Course Link

Day, time & location

SS20 online, link in course descricption.