Link to the course's web page: click here
Objectives:
Topological Data Analysis is an emerging trend in exploratory data analysis and data mining. It has known a growing interest and some notable successes in the recent years. The idea is to use topological tools to tackle challenging data sets, in particular data sets for which the observations lie on or close to nontrivial geometric structures that can fool classical techniques. Topological methods are indeed able to extract useful information about these geometric structures from the data, and to exploit that information to enhance the analysis pipeline.
Suggested readings:
Herbert Edelsbrunner and John Harer, Computational Topoogy: An Introduction, AMS press
S. Oudot. Persistence Theory: From Quiver Representations to Data Analysis. AMS Surveys and Monographs, Vol. 209, 2015
James R. Munkres. Elements of Algebraic Topology. Perseus, 1984
Language: The course material is in English. Lectures can be taught either in French or in English, at the students' convenience.
Evaluation: Final written exam, plus possibly one graded lab session.
Prerequisites :
in mathematics: a fair knowledge of linear and bilinear algebra, plus some notions of general topology. A background in statistics (MAP433) is desirable but not mandatory.
in computer science: some knowledge of algorithms (INF421) and programming (INF431 or INF442 or modal INF474A).
Feedback from this year's experience: if you are from MP, then this course is for you; if you are not from MP, then this course is also for you, although you will have to work harder; in any case, the goals remain achievable and are worth the effort! You can visit the course's webpage for further information, or contact the coordinator if you have any questions or concerns.
- Teaching coordinator: Li Jingyi
- Teaching coordinator: Mordacq Julie
- Teaching coordinator: Oudot Steve