Schedule: 09.09.2019 - 17.10.2019
Contact information for the course (applies in this implementation):
Teaching Period (valid 01.08.2018-31.07.2020):
I Autumn (2018-2019, 2019-2020)
Learning Outcomes (valid 01.08.2018-31.07.2020):
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.
Content (valid 01.08.2018-31.07.2020):
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 (valid 01.08.2018-31.07.2020):
Homework, project work
Workload (valid 01.08.2018-31.07.2020):
Contact hours 30-40h, independent work ca 100h
Study Material (valid 01.08.2018-31.07.2020):
Lecture notes, selected research articles, supplementary online material.
Course Homepage (valid 01.08.2018-31.07.2020):
Prerequisites (valid 01.08.2018-31.07.2020):
MS-C2111 Stochastic processes, MS-C1620 Statistical inference (recommended), MS-E1600 Probability theory (recommended)
Grading Scale (valid 01.08.2018-31.07.2020):
Details on the schedule (applies in this implementation):
- Lectures: Mon 12–14 @ Room M237 (Otakaari 1) and Thu 12–14 @ Room M3 (Otakaari 1)
- Exercises: Thu 14–16 @ Room Y307 (Otakaari 1)