INF631 -- Data Analysis: geometry and topology in arbitrary dimensions (2021-22)
Steve Oudot
General Introduction
There are many contexts in which
data have a geometric flavor. For instance, observations given in
the form of vectors living in Euclidean or Hilbert spaces, or as a
distance or dissimilarity matrix, are inherently geometric. For
such data, the quality of the analysis depends essentially on the
ability to uncover the geometric structures hidden in the
data. Furthermore, the geometry can be leveraged to speed up
bottlenecks in the analysis pipeline. This is where techniques
coming differential geometry, discrete and computational geometry,
algebraic or geometric topology, can help. The goal of this course
is to introduce the students to some of these techniques,
including the efficient computation of proximity queries,
reconstruction using Delaunay tessellations, metric-based
clustering methods, and topological tools for inference.
News
- [Sept. 20 2021] The Doodle for the choice of paper for the exam is up: check it out here.
- [Sept. 19 2021] This year we are switching back to normal mode. The lectures will take place in PC 17.
Practical Aspects
Where and when:
- lectures: Mondays 9:30 - 12:30, in PC 17
- practical sessions: Mondays 13:30 - 16:30, in PC 14
Important:
bring pens and paper to take notes.
Course grading:
Schedule
Internships
Feel free to come
and ask directly if you are looking for a research internship.
Last update: Sept. 19 2021.
(video file)