Title: Interaction of Geometry & Machine Learning
In this module of the Topics in Robotics PhD course, we will focus on exploring aspects of the role of geometry in machine learning. Three main topics, in particular, will be spectral theory on graphs, manifolds and generic data and applications to dimensionality reduction and geometric deep learning as well as low dimensional geometric representations for robotics applications (planning & RL).
Following the overview lectures (preliminary schedule below), pairs of participants will be required to define a small practical project that will involve presenting the underlying methodology for the project based on existing research (such as a specific research paper) and the groups will implement and evaluate their approach and present the results to the other participants.
fpokorny (at) kth.se
varava (at) kth.se
Preliminary Schedule (to be discussed/confirmed):
Zoom link (log in with your KTH credentials): https://kth-se.zoom.us/j/64500004938
Lecture 1 - Topology crash course: 2020-11-10, 13:00-15:00, via Zoom
Lecture 2 - Manifolds of various types: 2020-11-17, 13:00-15:00, via Zoom
Lecture 4 - Connections to dimensionality reduction in ML: 2020-12-08, 13:00-15:00, via Zoom
Lecture 5 - Geometric Deep Learning & Related Topics: 2020-12-15, 13:00-15:00, via Zoom
Lecture 6 - Dimensionality Reduction & Robotics Applications: 2020-12-?, ?-?, via Zoom
Project Proposal Presentation/Discussion: 2021-?-?, 13:00-15:00
Final Project Presentation Slots: 2021-03-?, 13:00-15:00