Postgraduate course 2020/2021

Research Seminar StaGe: Statistics, Geometry and their interaction with Graph Theory

Type: Elective course

Area of studies: Computer and Information Scienc

Delivered by: School of Data Analysis and Artificial Intelligence

Where: Faculty of Computer Science

When: 2 year, 1 semester

Mode of studies: offline

Instructors: Laurent Beaudou, Quentin Paris

Language: English

ECTS credits: 5

Contact hours: 38

Course Syllabus

Abstract

The course takes place every other week (once every two weeks) and each session lasts 2 academic hours. Sessions alternate between presentations by invited researchers and topical lectures on subjects ranging from Comparison Geometry and Optimal Transport to Graph Theory with a clear inclination towards questions of statistical nature.

Learning Objectives

• To provide doctoral students with theoretical background and exposure to current research topics presented by invited leading researchers, academic members of the computer science faculty or students themselves.

Expected Learning Outcomes

• Solve mathematically challenging problems.
• Work in group environment.
• Read, write and present research in mathematics and theoretical computer science.

Course Contents

• Geometry, Graph Theory, Probability and Statistics.

Assessment Elements

• Presentation
• Attendance
• Presentation
• Attendance

Interim Assessment

• Interim assessment (1 semester)
  0.5 * Attendance + 0.5 * Presentation

Bibliography

Recommended Core Bibliography

• Cesa-Bianchi, N., Lugosi, G. Prediction, learning, and games. – Cambridge university press, 2006. – 408 pp.

Recommended Additional Bibliography

• Diestel R. Graph Theory. – Springer, 2017. – 428 pp.