Computer Science - Decision Procedures

 

General information

Course name Decision Procedures
Course type Lecture
Course code Inf-EntVer
Course coordinator Prof. Dr. Dirk Nowotka
Faculty Engineering
Examination office pruefungsamt@informatik.uni-kiel.de
Short summary In this course we deal with essential decision procedures and their applications. Decision procedures for the satisfiability problem for various logics, SAT-, SMT, and QBF-solvers, are introduced. Such procedures constitute a technological foundation for applications in the field of software verification. We consider algorithmic aspects, heuristics and modern implementations of those.
   

Information about study level

Study level Master
Also possible for  
   

Information about credit points, evaluation and frequency

ECTS 6
Evaluation Oral exam
Frequency Winter semester
   

Information about teaching language

Teaching language English
Minimum language requirement B1
Further information on the teaching language  
   

Information about requirements

Recommended requirements Basic knowledge in mathematical logic
   

Information about course content, reading list and additional information

Course Content Algorithms, heuristics and modern implementations of decision procedures.
Reading list D. Kröning, O. Strichman: Decision Procedures - An Algorithmic Point of View (Springer, 2008)
Additional information  

 

Computer Science - Machine Learning

 

General information

Course name Machine Learning
Course type Lecture
Course code Inf-MaLearn
Course coordinator Prof. Dr. Carsten Meyer
Faculty Engineering
Examination office pruefungsamt@informatik.uni-kiel.de
Short summary Machine learning (a branch of artificial intelligence) is concerned with the design and development of algorithms that allow technical systems to solve tasks and to improve their performance by ("clever") learning from examples. The aim of this course is to provide a fundamental understanding of important concepts in machine learning, both from a theoretical and an application point of view. Several learning tasks (classification, regression, clustering), learning modes (supervised learning, unsupervised learning, reinforcement learning) and learning machines (support vector machine, perceptron, decision tree) are covered, in addition to methods for dimensionality reduction (principal component analysis, linear discriminant analysis) and algorithms for model selection and model combination (bagging, boosting).
   

Information about study level

Study level Master
Also possible for  
   

Information about credit points, evaluation and frequency

ECTS 6
Evaluation Oral exam
Frequency Summer term
   

Information about teaching language

Teaching language English
Minimum language requirement B1
Further information on the teaching language  
   

Information about requirements

Recommended requirements Mathematical basics of algebra and analysis and of optimization.
   

Information about course content, reading list and additional information

Course Content The following aspects will be covered in the lecture:
  1. Introduction, Machine learning basics
  2. Supervised classification: Support vector machines, decision trees, perceptron
  3. Unsupervised learning / clustering
  4. Dimensionality reduction: Linear discriminant analysis, principal component analysis
  5. Model selection
  6. Ensemble methods: Bagging, boosting
  7. Reinforcement learning
The exercises contain theoretical and practical exercises (based on available software libraries written in the python programming language) to deepen the understanding of the algorithms.
Reading list T. Mitchell, "Machine learning", McGraw Hill, 1997 E. Alpaydin, "Introduction to Machine Learning", MIT Press, 2010 S. Marsland, "Machine Learning: An Algorithmic Perspective", CRC Press, 2009 C. M. Bishop, "Pattern recognition and Machine learning", Springer, 2006 R. Duda et al., "Pattern classification", Wiley, 2001 S. Haykin, "Neural networks and learning machines", Prentice Hall, 2008
Additional information  

 

Computer Science - Software Architecture

 

General information

Course name Software Architecture
Course type Lecture
Course code Inf-SoftArch
Course coordinator Prof. Dr. Wilhelm Hasselbring
Faculty Engineering
Examination office pruefungsamt@informatik.uni-kiel.de
Short summary The fundamental concepts or properties of a system in its environment embodied in its elements, relationships, and in the principles of its design and evolution constitute the architecture of that system. An architecture description is used to express an architecture, for instance via an architecture description language. Software architecture intuitively denotes the high level structures of a software system. It can be defined as the set of structures needed to reason about the software system, which comprise the software elements, the relations between them, and the properties of both elements and relations. Documenting software architecture facilitates communication among stakeholders, captures early decisions about the high-level design, and allows reuse of design components between projects. With software systems architectures, we address the architecture of any complex system which may be of technical and sociotechnical nature. Critical system properties are, to a great extent, determined at the architectural level.
   

Information about study level

Study level Master
Also possible for  
   

Information about credit points, evaluation and frequency

ECTS 6
Evaluation Oral exam
Frequency Winter Semester
   

Information about teaching language

Teaching language English
Minimum language requirement B1
Further information on the teaching language  
   

Information about requirements

Recommended requirements Good knowledge of fundamental software engineering principles and practices.
   

Information about course content, reading list and additional information

Course Content Software architecture design, Quality requirements for Software Architectures, Architecture-Driven Modernization, Architectural styles
Reading list R. N. Taylor, N. Medvidovic and E. M. Dashofy. Software Architecture: foundations, theory and practice. Wiley. 2009
Additional information  

 

Electrical Engineering - Optimization and Optimal Control

 

General information

Course name Optimization and Optimal Control
Course type OPT
Course code etit-522
Course coordinator Prof. Dr.-Ing. habil. Thomas Meurer
Faculty Institute of Electrical Engineering and Information Technology
Examination office Electrical Engineering and Information Technology
Short summary The course gives an introduction to static and dynamic optimization without and with constraints as well as model-predictive control.
   

Information about study level

Study level Master
Also possible for  
   

Information about credit points, evaluation and frequency

ECTS 6 LP
Evaluation Oral exam (45 min.) during the examination period
Frequency Every winter term
   

Information about teaching language

Teaching language English
Minimum language requirement B1
Further information on the teaching language

 

 
   

Information about requirements

Recommended requirements Nonlinear Control Systems (module etit-501)
   

Information about course content, reading list and additional information

Course Content
  • Fundamentals of static and dynamic optimization problems
  • Static optimization without and with constraints
  • Dynamic optimization without and with constraints
  • Model-predictive control
Reading list
  • T. Meurer: Optimization and Optimal Control, Lecture notes.
  • S. Boyd, L. Vandenberghe: Convex Optimization, Cambridge University Press.
  • A.E. Bryson: Dynamic Optimization, Addison-Wesley.
  • L. Grüne, J. Pannek: Nonlinear Model Predictive Control: Theory and Algorithms, Springer.
  • D.G. Luenberger, Y. Ye: Linear and Nonlinear Programming, Springer.
  • J. Nocedal, S.J. Wright: Numerical Optimization, Springer.
  • M. Papageorgiou: Optimierung, Oldenbourg Verlag.
Additional information  

 

Begleitseminar Europa macht Schule

 

Allgemeine Information

Kursname  Begleitseminar Europa macht Schule
Kurstyp  Seminar
Kurscode  200248
Kursverantwortlicher  Frau Melanie Korn, Herr Danny Drefahl
Fakultät  Philosophische Fakultät
Prüfungsamt  Gemeinsames Prüfungsamt
Kurzzusammenfassung Im „Europa macht Schule“-Begleitseminar bekommen Erasmus-Studierende die nötige Vorbereitung, um ihr Projekt im Rahmen von „Europa macht Schule“ erfolgreich durchführen zu können. Es werden Grundlagen der Didaktik, Methodik, sowie der Projektplanung vermittelt. Im Zuge dessen werden die verschiedenen Schulsysteme in Europa herangezogen und kritisch hinterfragt.
   

Information über das Studienniveau

Studienniveau  BA,MA
Möglich auch für  -
   

Informationen über Leistungspunkte, Bewertung und Angebotshäufigkeit

ECTS  2,5
Bewertung  Portfolio
Angebotshäufigkeit  Semesterweise
   

Information über die Lehrsprache

Lehrsprache  Deutsch (+ Englisch)
Mindestanforderung  Keine
Näheres zur Lehrsprache  -
   

Information über die Zugangsvoraussetzungen

Empfohlene Zugangsvoraussetzungen  Deutschkenntnisse wünschenswert
   

Informationen über Lehrinhalte, Literatur und weitere Angaben

Lehrinhalte  Grundlagen der Didaktik, Methodik, Projektplanung etc.
Literatur  -
Weitere Angaben  Obligatorisch ist die Teilnahme am Programm „Europa macht Schule“ www.europamachtschule.de

 

Materials Science - Nanomedicine

 

General information

Course name Nanomedicine
Course type Modul (Lecture and Exercise)
Course code Mawi 930
Course coordinator Prof. Dr. C. Selhuber-Unkel / Dr. O. Riemenschneider
Faculty Faculty of Engineering
Examination office Examination Office for Materials Science
Short summary This module will convey an overview of the highly interdisciplinary field of nanomedicine and the biomedical application of novel multifunctional nanomaterials.
   

Information about study level

Study level Master
Also possible for  
   

Information about credit points, evaluation and frequency

ECTS 3
Evaluation  
Frequency Winter and Summer semester
   

Information about teaching language

Teaching language English
Minimum language requirement B1
Further information on the teaching language  
   

Information about requirements

Recommended requirements Knowledge about magnetism, and cell biology are helpful
   

Information about course content, reading list and additional information

Course Content This module will convey an overview of the highly interdisciplinary field of nanomedicine and the biomedical application of novel multifunctional nanomaterials. A special focus will lie on magnetic particles and biosensors. Basic principles and applications will be introduced. Representative nanoparticles and sensors will be discussed in more detail with regrads to their overall properties, distinctive features and specific biomedical use.
Reading list Nanomedicine: Design and Applications of Magnetic Nanomaterials, Nanosensors and Nanosystems Vijay K. Varadan, Dr LinFeng Chen, Jining Xie ISBN: 978-0-470-03351-7   Further literature will be suggested during the course.
Additional information   Workload: 30 h seminar (course attendance) 15 h exercise (self-organized studies)   Assessment of course achievements   The students will gain knowledge about: -           Biomedical application of nanoparticles -           Techniques needed for biomedical research -           State of the art in development and application of magnetic nanomaterials and sensors in medicine -           Requirements for the design of biocompatible materials according to biomedical requirements   The students will acquire competences regarding: -           interdisciplinary work and language -           extracting information from interdisciplinary papers -           designing interdisciplinary projects

 

Material Science - Smart Materials

 

General information

Course name Smart Materials
Course type Modul (Lecture and Exercise)
Course code Mawi 909
Course coordinator Prof. Dr. E. Quandt / Dr. O. Riemenschneider
Faculty Faculty of Engineering
Examination office Examination Office for Materials Science
Short summary The students will be introduced into the domain of smart materials.
   

Information about study level

Study level Master
Also possible for  
   

Information about credit points, evaluation and frequency

ECTS 6
Evaluation  
Frequency Winter and Summer Semester
   

Information about teaching language

Teaching language English
Minimum language requirement B1
Further information on the teaching language  
   

Information about requirements

Recommended requirements Basics in solid state physics Basics in materials science
   

Information about course content, reading list and additional information

Course Content Smart Materials - Classification - Application Areas Piezoelectric Materials - Piezoeffect - Piezoelectric Materials - Ferroelectricity - Fabrication - Applications Magnetostrictive Materials - Magnetostriction - Cryogenic Materials - Rare Earth - Fe phases - Thin Film Materials - Applications Shape Memory Alloys - Shape Memory Effects - Superelasticity - TiNi - based materials - Shape Memory Thin Films - Applications Multiferroic Materials - Magnetic Shape Memory Materials - Magnetoelectric Composites
Reading list •          K. Uchino, Ferroelectric Devices, New York: Marcel Dekker, 2000 •          Giant magnetostrictive materials: physics and device applications, Ed: G. Engdahl. San Diego: Academic Press, 2000 •          C. M. Wayman und K. Otsuka, Shape Memory Materials, Cambridge University Press, 1999
Additional information Workload: 30 h lecture (course attendance) 15 h exercise (course attendance) 45 h exercise (self-organized studies) 30 h lecture (revision)   Learning outcome: Knowledge The students will be introduced into the domain of smart materials. Skills The students will understand the correlation between composition, microstructure and properties of smart and multiferroic materials. Competences Students will get a compendium over smart materials for understanding new approaches to materials sciences problems. The students will have learned scientific purchase as well as bulk fabrication rules.

 

Material Science - Advanced Materials A

 

General information

Course name Advanced Materials A
Course type Modul (Lecture and Exercise)
Course code Mawi 705
Course coordinator Prof. Dr. F. Faupel / Dr. O. Riemenschneider
Faculty Faculty of Engineering
Examination office  
Short summary Advanced Materials A - Metals Advanced Materials A - Polymers
   

Information about study level

Study level Master
Also possible for  
   

Information about credit points, evaluation and frequency

ECTS 8 (6 SWS)
Evaluation  
Frequency Winter and Summer Semester
   

Information about teaching language

Teaching language English
Minimum language requirement B1
Further information on the teaching language  
   

Information about requirements

Recommended requirements Basic lecture mathematics Basic lecture physics Basic lecture chemistry
   

Information about course content, reading list and additional information

Course Content Metals   Alloys Thermodynamic considerations Intermetallic phases Mechanical Properties Plastic deformation in single crystals via dislocations Deformation twinning Deformation of polycrystals Creep Fracture Solid solution hardening Thermally Activated Processes Diffusion Recrystallization Solidification of Metallic Melts Transformation in the Solid State Particle Hardened Alloys   Polymers   Properties and Classification of Plastics Binding Forces and Structure Polymer Synthesis Polymers in Melts and Solutions Thermodynamics and chain kinetics Crystallization and Glass Formation Mechanical Properties Dielectric and Optical Properties Conducting Polymers Sorption, Diffusion and Permeation Chemical and Physical Aging, Recycling Plastics technology
Reading list

•    P. Haasen, Physical Metallurgy, Cambridge University Press, Cambridge 1996 (German edition available)

•    K. Easterling, Modern Physical Metallurgy, Butterworths 1983

•    Cottrell, An Introduction to Metallurgy, The Institute of Metals 1995 (reprint at 1975 edition)

•    N. Stoloff, Physical Metallurgy and Processing, Chapman 1994

•    G. Gottstein, Physikalische Grundlagen der Materialkunde, Springer 1998 (German)

•    H. Böhm, Einführung in die Metallkunde, B. I. 1992 (German)

•    E. Hornbogen und H. Warlimont, Einführung in die Metallkunde, Springer 1991 (German)

•    R.E. Reed-Hill and R. Abbaschian, Physical Metallurgy Principles, PWS-Kent 1992

•    R.E. Smallman and R.J. Bishop, Modern Physical Metallurgy of Materials Engineering, Butterworth/Heinemann/1999

•    R. Cahn und P. Haasen (Eds.), Physical Metallurgy, Elsevier Science 1996

•    R.J. Young, P.A. Lovell: Introduction to Polymers, Chapman & Hall 1991.

•    L.H. Sperling: Introduction to Physical Polymer Science, John Wiley 1992.

•    U. Eisele: Introduction to Polymer Physics, Springer 1990.

•    N.G. McCrum, C.P. Buckley, C.B. Bucknall, Principles of Polymer Engineering, Oxford Science Publications 1995.

•    G. Menges: Werkstoffkunde Kunststoffe, Hanser 1990 (German)

•    G. W. Ehrenstein: Polymerwerkstoffe, Hanser 1978 (German)

W. Retting, H.M.Laun: Kunststoffphysik, Hanser 1991 (German).

Additional information Workload: 60 h lecture (course attendance) 30 h exercise (course attendance) 90 h exercise (self-organized studies) 60 h lecture (revision)   Learning Outcome: Knowledge The module aims at making the students familiar with the relation between structure and resulting properties of metallic and organic materials. Emphasis will be placed on mechanical properties.   Skills The students will learn how to apply their knowledge on basic materials science and on solid state physics to understanding the design of advanced metallic and organic materials. Competences The students will be able to understand the current literature on metallic and organic materials and to deal with them in research, development, and production.

 

Material Science - Advanced Materials B

 

General information

Course name Advanced Materials B
Course type Modul (Lecture + Excerise)
Course code Mawi 706
Course coordinator Prof. Dr. J. McCord / Dr. O. Riemenschneider
Faculty Faculty of Engineering
Examination office Examination Office for Materials Science
Short summary Students will understand the abundance of electronic materials spanning the range from semiconductors to ceramics and including “simple” topics like conductors and magnetic materials.
   

Information about study level

Study level Master
Also possible for  
   

Information about credit points, evaluation and frequency

ECTS 8 (6 SWS)
Evaluation  
Frequency Winter and Summer Semester
   

Information about teaching language

Teaching language English
Minimum language requirement B1
Further information on the teaching language  
   

Information about requirements

Recommended requirements Basics materials science Basics in  semiconductors technology Basics in advanced mathematics
   

Information about course content, reading list and additional information

Course Content Electronic Materials   Conductors Ionic conductors and their applications Thermoelectricity Transparent conductors. Theory of dielectrics Polarization mechanisms Frequency behaviour Complex dielectric function Complex index of refraction Ferroelectricity. Basic optics Fresnel equations Complex index of refraction and optical properties, Optical communication Lasers and optical modes. Theory of magnetism Dia-, para- and  ferromagnetism Mean field theory of ferromagnetism Domain structure Hysteresis. Fundamentals of semiconductor processing Single crystal growth Essential processes and limitations   Ceramics   Ceramics processing Bulk and thin film techniques Sintering, sputtering and other processing Microstructure Mechanical and thermal properties Ferroelectric Piezoelectric Electrooptic materials Pyroelectrical behaviour Ceramic conductors Ceramic superconductors Magnetic and magnetoelectric ceramics and nanocompounds
Reading list •          L.A.A. Warnes: Electronic Materials •          R.E. Hummel: Electronic Properties of Materials •          Kingery, W.D., Bowen, H.K., Uhlmann, D.R.: Introduction to Ceramics, Wiley-Interscience, New York •          Moulson, A.J., Herbert, J. M.: Electroceramics (Materials, Properties, Applications); Chapman & Hall, London •          Steele, B.C. H. (Hrsg.): Electronic Ceramics; Elsevier Applied Science, London •          Schaumburg, H. (Hrsg.): Keramik; B.G. Teubner, Stuttgart •          Hench, L.L., West, J.K.: Principles of Electronic Ceramics; Wiley-Interscience, New York •          Internet Script: http://www.tf.uni-kiel.de/matwis/amat/elmat_en/index.html
Additional information Workload: 60 h lecture (course attendance) 30 h exercise (course attendance) 90 h exercise (self-organized studies) 60 h lecture (revision)   Learning Outcome: Knowledge Students will understand the abundance of electronic materials spanning the range from semiconductors to ceramics and including “simple” topics like conductors and magnetic materials. Skills They will learn that technology is intimately linked to properties and functions and apply this knowledge to the functions and the making of devices like Si chips, sensors, solar cells, thermoelectric, magnetic and nano compound devices. Competences Students will get a solid background in general theory which enables them to quickly adapt to new materials, concepts