Search result: Catalogue data in Spring Semester 2015

Agroecosystem Science Master Information
Majors According
Major in Food and Resource Use Economics
Methodology Competences
Methods in Food and Resource Use Economics
NumberTitleTypeECTSHoursLecturers
363-0588-00LComplex Networks Information W4 credits2V + 1UF. Schweitzer, I. Scholtes
AbstractThe course provides an overview of the methods and abstractions used in (i) the quantitative study of complex networks, (ii) empirical network analysis, (iii) the study of dynamical processes in networked systems, (iv) the analysis of systemic risk in networked systems, (v) the study of network evolution, and (vi) data mining techniques for networked data sets.
Objective* the network approach to complex systems, where actors are represented as nodes and interactions are represented as links
* learn about structural properties of classes of networks
* learn about feedback mechanism in the formation of networks
* understand systemic risk as emergent property in networked systems
* learn about statistical inference techniques for data on networked systems
* learn methods and abstractions used in the growing literature on complex networks
ContentNetworks matter! This holds for social and economic systems, for technical infrastructures as well as for information systems. Increasingly, these networked systems are outside the control of a centralized authority but rather evolve in a distributed and self-organized way. How can we understand their evolution and what are the local processes that shape their global features? How does their topology influence dynamical processes like diffusion? And how can we characterize the importance and/or role of specific nodes? This course provides a systematic answer to such questions, by developing methods and tools which can be applied to networks in diverse areas like infrastructure, communication, information systems or (online) social networks. In a network approach, agents in such systems (like e.g. humans, computers, documents, power plants, biological or financial entities) are represented as nodes, whereas their interactions are represented as links.

The first part of the course, "Introduction to networks: basic and advanced metrics", describes how networks can be represented mathematically and how the properties of their link structures can be quantified empirically.

In a second part "Stochastic Models of Complex Networks" we address how analytical statements about crucial properties like connectedness or robustness can be made based on simple macroscopic stochastic models without knowing the details of a topology.

In the third part we address "Dynamical processes on complex networks". We show how a simple model for a random walk in networks can give insights into the authority of nodes, the efficiency of diffusion processes as well as the existence of community structures.

A fourth part "Statistical Physics of Networks: Optimisation and Inference" introduces models for the emergence of complex topological features which are due to stochastic optimization processes, as well as algorithmic approaches to automatically infer knowledge about structures and patterns from network data sets.

In a fifth part, we address "Network Dynamics", introducing models for the emergence of complex features that are due to (i) feedback phenomena in simple network growth processes or (iii) order correlations in systems with highly dynamic links.

A final part studies "Multiple roles of nodes and links", introducing recent research on automated role discovery in networks, as well as models for networks with multiple layers.
Lecture notesThe lecture slides are provided as handouts - including notes and literature sources - to registered students only.
All material is to be found on Moodle at the following URL: Link
LiteratureSee handouts. Specific literature is provided for download - for registered students, only.
Prerequisites / NoticeThere are no pre-requisites for this course. Self-study tasks (to be solved analytically and by means of computer simulations) are provided as home. Weekly exercises (45 min) are used to discuss selected solutions. Active participation in the exercises is strongly suggested for a successful completion of the final exam.
Project Management and Communication of Science
NumberTitleTypeECTSHoursLecturers
751-1000-00LInterdisciplinary Project Work Information Restricted registration - show details
Prerequisite: successful completion of the bachelor programme.
O3 credits4UB. Dorn, E. Frossard, L. Meile, H. Adelmann, N. Buchmann, C. De Moraes, P. A. Fischer, M. C. Härdi-Landerer, M. Kreuzer, U. Merz, S. Peter, M. Schuppler, M. Siegrist, J. Six, S. E. Ulbrich, A. Walter
AbstractDie Studierenden der Agrar- und Lebensmittelwissenschaften erarbeiten in interdisziplinären Teams Lösungen für Probleme, welche ihnen von Projektpartner im Bereich der Nahrungsmittelwertschöpfungskette gestellt werden.
ObjectiveDie Studierenden kennen
- die Grundlagen des Zeit- und Projektmanagements
- Vorgehensweisen, um Probleme, die ihnen von Projektpartnern gestellt werden, zielorientiert zu lösen.
ContentDie Studierenden der Agrar- und Lebensmittelwissenschaft erarbeiten in interdisziplinären Teams Lösungen für Probleme, welche ihnen von Projektpartnern entlang der Nahrungsmittelwertschöpfungskette gestellt werden. Die Studierenden präsentieren und diskutieren die Lösungsvorschläge an der Schlussveranstaltung mit den Projektpartnern und verfassen einen schriftlichen Projektbericht.
Prerequisites / NoticeDie Anwesenheit der Studierenden an der Startveranstaltung am 26.2.2015 gemäss speziellem Programm ist Pflicht.
751-2901-00LResearch Project in FRE Restricted registration - show details W2 credits4AM. Dumondel
AbstractThe student will work together with a PhD student on a topic with emphasis on 'Swissness of Swiss Food'.
ObjectiveThe student will work together with a PhD student on a specific research field
ContentThe student will work together with a PhD student on a specific research field
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