Seminar Geometry and Topology in Deep Learning
With machine learning making increasing use of foundational principles from geometry and topology, this seminar provides a glimpse into the state of the art. We will discuss architectures and use cases of models leveraging both geometry and topology to address complex tasks, including graph classification, graph regression and general shape analysis.
Details
| Code |
63612 |
| Type |
Seminar |
| ECTS |
5 |
| Site |
Fribourg |
| Track(s) |
T6 – Data Science
|
| Semester |
A2024 |
Teaching
| Learning Outcomes |
The primary goal of this seminar is for students to learn how to approach cutting-edge literature in machine learning research. Students will learn how to read and analyse papers, followed by a brief report on a specific body of work. Moreover, students will be given the opportunity to (re)implement methods described in the paper, using a programming language of their
choice. Successful participation in this seminar will enable students to undertake small guided research on their own.
|
| Lecturer(s) |
Bastian Grossenbacher-Rieck
|
| Language |
english |
| Course Page |
The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_3102208.html.
|
Schedules and Rooms
| Period |
On Appointment |
| Location |
UniFR, PER21 |
Evaluation
| Evaluation type |
continuous evaluation |
Additional information
| Comment |
First Lecture
The first lecture will take place on Wednesday, 30.10.2024 at 09:00 in UniFR, PER21.
|
