Geodesically convex optimization has recently attracted attention from the machine learning community due to the realization that some important problems that appear to be non-convex at first glance, are geodesically convex, if we introduce a suitable differential structure and a metric. In this thesis, the student is expected to conduct an empirical study on the performance of geodesic convex optimization methods for applications in machine learning, compared to the state-of-the-art non-convex optimization techniques.
- English language
- successfully completed courses Machine Learning I and II
- Programming skills in Python
- Knowledge in Differential Geometry and Convex Optimisation