Curriculum on Self-Organizing Networked Systems

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Curriculum

Core Courses

The following courses are mandatory. About 60 ECTS.

Introduction to Self-Organizing Networked Systems

  • Part I (30h): What is a self-organizing system? Methodology and theory. Giving the big picture. Few mathematics, non-technical. History. Evolution, cybernetics, chaos, emergence, entropy. Links to following courses. Similar to course "complexity and evolution" in Brussels.
  • Part II: Case studies. A ring lecture (colloquium) with speakers from different universities and different fields (which can also attended by all university members):
    • Self-Organization in Communication and Computer Networks
    • Social insects
    • Immune system
    • Social group dynamics
    • Self-Organization in Biology
    • Brain dynamics
    • Robotics
    • Chemical reactions
    • Self-organization in linguistics
    • Overview of cellular and evolutionary biology

Dynamical Systems

Lecture and Lab

Content: similar to "Nonlinear Dynamics and Chaos" by Steven H. Strogatz

Textbooks:

Network Theory

Lecture and Exercises

Content: Networks from the real world. Network topology: Graph theory basics, random graphs, phenomena small wold and scale-freeness. Network functions/processes/algorithms: E.g. search, percolation.

Textbooks:

Information and Communication Theory

Lecture and Exercises

Textbooks:

Stochastics and Statistical Physics

Lecture and Exercises

Content: Selected topics from the following fields: Review of random variables, combination of RVs, Stochastic Processes (Concepts, random walks and other applications, Markov chains, Markov processes and queueing theory). Statistical Physics .

Textbooks:

Intelligent Systems

Neural networks: introduction to biological neural networks, Artificial Neural Networks, topologies (Multilayer, recurrent, fully meshed), Self-Organizing Feature Maps, spiking neural networks, emergent patterns in ANN

Machine learning: Genetic algorithms, simulated annealing, Sintflut algorithm, rule-based learning, bayesian networks

Modeling and Simulation

Contents:

  • Model building, modeling and specification languages (e.g. UML)
  • How to do good simulations and data analysis, model validation?
  • How to do good empirical studies?
  • Data/results representation
  • Cellular automata, evolutionary algorithms, multi-agent systems, PDEs
  • Group work

Related books:

Algorithms and Data Structures

Lecture and Lab.

Content: Sorting and searching, tree-based structures, graph algorithms (over), recursive algorithms, complexity classes and computational effort.

Textbooks:

Introduction to Control Theory and Optimization

control loop, stability, distributed control, event-based control, MIMO control systems

Social dynamics

  • Game theory: evolution of cooperation (Prisoner's Dilemma), implications for economic behavior
  • Group dynamics: dynamics of consensus and conflict in groups of individuals trying to solve problems together; includes emergent processes such as polarization, groupthink, coordination of language and terminology
  • Collective intelligence: conditions under which a group can make better/worse decisions than the individuals it is composed of

Catch-up Courses

Depending on the background of the student, she or he attends a subset of the following courses:

Specialization 1: Communication Networks

Mobile and Wireless Systems

Contents: Introduction and overview. Radio propagation. Coding, modulation, and duplexing. Multiple access and cellular concept. Medium access control (MAC) protocols. Wireless LAN 802.11. Network architecture and mobility protocols. Security in mobile networks. Multihop networks. Economic, health, and social aspects.

Sensor Networks

Peer-to-Peer Networks

Protocol Engineering

Group Work

Curriculum table

Subject Type g1 grad g2 und.grad g2 grad g2 grad spec. g3 g4
Dynamical Systems Lecture X X X
Graph and Network Theory X X
Optimization X
Probability and Stochastic X
Information Theory Lecture X X
Algorithms 1 Lecture & Lab X
Algorithms 2 Lecture & Lab X
Distributed Algorithms X X
Game Theory X X
Numerical Simulations Lecture & Labs X
Modeling and Simulation Lecture & Labs X
Topics course SO in nature/society Lecture X X
Network 1 X X
Network 2 X X
Statistical physics 1 & 2 X X
Embedded Systems X
Sensors and Robotics X
Calculus 1 & 2 Lecture X X
Statistics Lecture X
Diff. Equations Lecture X X
Linear Algebra Lecture X
Scientific Programming Lecture X X
Natural sciences Lecture X X
Mobile&Wireless Systems X
Sensor Networks X
Peer-to-Peer Networks X
Information Theory and Coding X X
Protocol Engineering X
Physics bacc
Applied Mathematics bacc
Biology bacc X
Economics X
Business management X
Conceptual Framework X
Ethics (Philosophical issues) X
Bayesian statistics X
Neural networks X

Group 1: Bauschert, Bettstetter, Pletzer, Quaritsch, Yanmaz

[[Curriculum-so-work.jpg]]


Group 2: Anton, Manfred, Felix, Johannes,Alain

All courses should specifically deal with applications towards SOS.

Group 3: Francis, Marc, Mariam, Nikolaj, Raissa,

For Master program the requirements are basics of differential equations, calculus, PDEs.

Comments to some courses:

Computational techniques: agent-based models, genetic algorithms, cellular automata.

Statistical Physics: phase transition, entropy, combinatorics/ensembles.

Biology: focus on cellular and evolutionary biology.

Distributed control and communications.

Tools (computational): CellSim, R, virtual laboratory, as well as for visualizing dynamical systems, basins of attraction.

Mathematical tools: MAtheatica, Matlab.

Group work methods, project-based learning, Wiki.

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