Difference between revisions of "Curriculum on Self-Organizing Networked Systems"

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(Intelligent Systems (?))
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====Intelligent Systems (?) ====
 
====Intelligent Systems (?) ====
Content: Game theory, neural networks, machine learning
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Game theory: Cooperation (Prisoneer's Dilemma), economic behavior
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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
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Machine learning: Genetic algorithms, simulated anneahling, Sintflut algorithm, rule-based learning, bayesian networks
  
 
====Modeling and Simulation (?)====
 
====Modeling and Simulation (?)====

Revision as of 13:26, 16 July 2009

Curriculum

Core Courses

The following courses are mandatory:

Introduction to Self-Organizing Networked Systems

  • Part I: What is a self-organizing system? Methodology and theory. Links to following courses.
  • Part II: Case studies. A ring lecture with speakers from different universities and different fields.

Dynamic 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.

Information Theory and Coding

Lecture and Exercises

Advanced Stochastics

Lecture and Exercises

Content: Selected topics from the following fields: Stochastic Processes. Statistical Physics.

Intelligent Systems (?)

Game theory: Cooperation (Prisoneer's Dilemma), economic behavior

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 anneahling, Sintflut algorithm, rule-based learning, bayesian networks

Modeling and Simulation (?)

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:

Control Theory

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

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

Sensor Networks

Peer-to-Peer Networks

Protocol Engineering

Group Work

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.

Curriculum table

Subject Type g1 grad g2 und.grad g2 grad g2 grad spec. g3 g4
Dynamical Systems Lecture X
Information Theory Lecture X
Algorithms 1 Lecture & Lab X
Algorithms 2 Lecture & Lab X
Numerical Simulations Lecture & Labs X
Topics course SO in nature/society Lecture X
Network 1 X
Network 2 X
Statistical physics 1 & 2 X
Embedded Systems X
Sensors and Robotics X
Calculus 1 & 2 Lecture X
Statistics Lecture X
Diff. Equations Lecture X
Linear Algebra Lecture X
Programming Lecture X
Natural sciences Lecture X
Mobile&Wireless Systems X
Sensor Networks X
Peer-to-Peer Networks X
Information Theory and Coding X
Protocol Engineering X
Physics bacc
Applied Mathematics bacc
Biology bacc

}