Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
The goal of the course is to get introduced to key statistical network concepts and their mathematical foundations, the theory of random graphs. The course is targeted for students in mathematics, operations research, and computer science, with interest in probability, graphs, and networks. You will become familiar with basic statistical models used to model unknown network structures. You will gain insight into the type of structural network properties that can be learned from a single graph sample. You will learn to investigate how well an observed graph fits a given statistical model by comparing observed and theoretical graphlet densities. You learn to apply probabilistic inequalities for recognizing almost surely occurring events in large random systems.
Schedule: 07.09.2020 - 15.10.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Lasse Leskelä
Teacher in charge (applies in this implementation): Lasse Leskelä
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
The first part of the course focuses on random graph models and their mathematical analysis. The second part focuses on the learning and identifiability of graph parameters and structural properties from observed network data.
Assessment Methods and Criteria
Homeworks and project work
Contact hours 30-40h, independent work ca 100h
Lecture notes, selected research articles, supplementary online material.
MS-C2111 Stochastic processes, MS-C1620 Statistical inference (recommended), MS-E1600 Probability theory (recommended)
- Teacher: Lasse Leskelä