Type of studies | Title |
---|---|
Master Academic Studies | Artificial Intelligence and Machine Learning (Year: 1, Semester: Winter) |
Master Academic Studies | Mathematics in Engineering (Year: 1, Semester: Winter) |
Master Academic Studies | Information and Analytics Engineering (Year: 1, Semester: Winter) |
Master Academic Studies | Information Engineering (Year: 1, Semester: Winter) |
Category | Scientific-professional |
Scientific or art field | Telecommunications and Signal Processing |
ECTS | 6 |
Ability to apply the learned tools to model, analyze and solve a given real-world problem from the domain of networks. Ability to implement the learned methods and algorithms in a chosen experimental setup: USRP testbed or MATLAB/Python network data analysis software.
After successful completion of the course, the students will be able to: 1) apply the learned tools to model, analyze and solve a given real-world problem from the domain of networks; 2) implement the learned methods and algorithms in a chosen experimental setup: USRP testbed or MATLAB / Python network data analysis software.
: Introduction to graph theory and algebraic graph theory, Adjacency matrix and graph Laplacian; Degree distribution and scale – free property; Clustering coefficients and centrality measures; Models of network formation: Erdos-Renyi random graph, Watts-Strogatz networks, Barabasi-Albert; Network communities and algorithms for their detection, stochastic block-model; Consensus algorithms, Industrial take on network science; Students’ project presentations and discussions.
Lectures, recitations, case study examples, lab sessions, reading assignments, homeworks, project This course is designed to balance and complement a principled, theoretical approach to network science with a highly practical and goal-oriented approach. In particular, the course will present the underlying theory in a mathematically rigorous manner (with carefully chosen proof derivations), but every new concept will be introduced, motivated and illustrated through a real-world problem and example. Further, there will be bi-weekly slots in the duration of one hour dedicated to detailed real-world case studies with emphasis on the most recent topics covered in lectures (WWW, Financial networks, Internet, etc., see [4]). Finally, lab sessions will be designed as hands-on tutorials for the relevant lab equipment (USRP testbed, software), which will gradually shift towards students’ individual, project driven work as the course progresses.
Authors | Title | Year | Publisher | Language |
---|---|---|---|---|
2018 | English | |||
2016 | English | |||
2015 | English | |||
2016 | English |
Course activity | Pre-examination | Obligations | Number of points |
---|---|---|---|
Written part of the exam - tasks and theory | No | Yes | 50.00 |
Project | Yes | Yes | 50.00 |
Associate Professor
Assistant - Master
Assistant - Master
Intern Researcher
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© 2024. Faculty of Technical Sciences.