Suchergebnis: Katalogdaten im Herbstsemester 2022

Elektrotechnik und Informationstechnologie Master Information
Master-Studium (Studienreglement 2008)
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Systems and Control
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NummerTitelTypECTSUmfangDozierende
227-0102-00LDiskrete Ereignissysteme Information W6 KP4GL. Josipovic, L. Vanbever, R. Wattenhofer
KurzbeschreibungEinführung in Diskrete Ereignissysteme (DES). Zuerst studieren wir populäre Modelle für DES. Im zweiten Teil analysieren wir DES, aus einer Average-Case und einer Worst-Case Sicht. Stichworte: Automaten und Sprachen, Spezifikationsmodelle, Stochastische DES, Worst-Case Ereignissysteme, Verifikation, Netzwerkalgebra.
LernzielOver the past few decades the rapid evolution of computing, communication, and information technologies has brought about the proliferation of new dynamic systems. A significant part of activity in these systems is governed by operational rules designed by humans. The dynamics of these systems are characterized by asynchronous occurrences of discrete events, some controlled (e.g. hitting a keyboard key, sending a message), some not (e.g. spontaneous failure, packet loss).

The mathematical arsenal centered around differential equations that has been employed in systems engineering to model and study processes governed by the laws of nature is often inadequate or inappropriate for discrete event systems. The challenge is to develop new modeling frameworks, analysis techniques, design tools, testing methods, and optimization processes for this new generation of systems.

In this lecture we give an introduction to discrete event systems. We start out the course by studying popular models of discrete event systems, such as automata and Petri nets. In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an average-case perspective: we model discrete events as stochastic processes, and then apply Markov chains and queuing theory for an understanding of the typical behavior of a system. In the last part of the course we analyze discrete event systems from a worst-case perspective using the theory of online algorithms and adversarial queuing.
Inhalt1. Introduction
2. Automata and Languages
3. Smarter Automata
4. Specification Models
5. Stochastic Discrete Event Systems
6. Worst-Case Event Systems
7. Network Calculus
SkriptAvailable
Literatur[bertsekas] Data Networks
Dimitri Bersekas, Robert Gallager
Prentice Hall, 1991, ISBN: 0132009161

[borodin] Online Computation and Competitive Analysis
Allan Borodin, Ran El-Yaniv.
Cambridge University Press, 1998

[boudec] Network Calculus
J.-Y. Le Boudec, P. Thiran
Springer, 2001

[cassandras] Introduction to Discrete Event Systems
Christos Cassandras, Stéphane Lafortune.
Kluwer Academic Publishers, 1999, ISBN 0-7923-8609-4

[fiat] Online Algorithms: The State of the Art
A. Fiat and G. Woeginger

[hochbaum] Approximation Algorithms for NP-hard Problems (Chapter 13 by S. Irani, A. Karlin)
D. Hochbaum

[schickinger] Diskrete Strukturen (Band 2: Wahrscheinlichkeitstheorie und Statistik)
T. Schickinger, A. Steger
Springer, Berlin, 2001

[sipser] Introduction to the Theory of Computation
Michael Sipser.
PWS Publishing Company, 1996, ISBN 053494728X
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UE. Konukoglu, F. Yu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThis course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning.
The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer.
The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux.
The course language is English.
227-0526-00LPower System AnalysisW6 KP4GG. Hug
KurzbeschreibungZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge. Die Herleitung der stationären Modelle der Komponenten des elektrischen Netzes, die Aufstellung der mathematischen Gleichungssysteme, deren spezielle Charakteristiken und Lösungsmethoden stehen im Vordergrund.
LernzielZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge und die Anwendung von Analysemethoden in stationären und dynamischen Zuständen des elektrischen Netzes.
InhaltDer Kurs beinhaltet die Herleitung von stationären und dynamischen Modellen des elektrischen Netzwerks, deren mathematische Darstellungen und spezielle Charakteristiken sowie Lösungsmethoden für die Behandlung von grossen linearen und nichtlinearen Gleichungssystemen im Zusammenhang mit dem elektrischen Netz. Ansätze wie der Netwon-Raphson Algorithmus angewendet auf die Lastflussgleichungen, Superpositions Prinzip für Kurzschlussberechnung, Methoden für Stabilitätsanalysen und Lastflussberechnungsmethoden für das Verteilnetz werden präsentiert.
SkriptVorlesungsskript.
227-0689-00LSystem IdentificationW4 KP2V + 1UR. Smith
KurzbeschreibungTheory and techniques for the identification of dynamic models from experimentally obtained system input-output data.
LernzielTo provide a series of practical techniques for the development of dynamical models from experimental data, with the emphasis being on the development of models suitable for feedback control design purposes. To provide sufficient theory to enable the practitioner to understand the trade-offs between model accuracy, data quality and data quantity.
InhaltIntroduction to modeling: Black-box and grey-box models; Parametric and non-parametric models; ARX, ARMAX (etc.) models.

Predictive, open-loop, black-box identification methods. Time and frequency domain methods. Subspace identification methods.

Optimal experimental design, Cramer-Rao bounds, input signal design.

Parametric identification methods. On-line and batch approaches.

Closed-loop identification strategies. Trade-off between controller performance and information available for identification.
Literatur"System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999.

Additional papers will be available via the course Moodle.
Voraussetzungen / BesonderesControl systems (227-0216-00L) or equivalent.
227-0945-00LCell and Molecular Biology for Engineers I
Findet dieses Semester nicht statt.
W3 KP2GNoch nicht bekannt
KurzbeschreibungThe course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology.
LernzielAfter completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested.
InhaltLectures will include the following topics (part I and II): DNA, chromosomes, genome engineering, RNA, proteins, genetics, synthetic biology, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer and stem cells.

In addition, 4 journal clubs will be held, where recent publications will be discussed (2 journal clubs in part I and 2 journal clubs in part II). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 40% for the final grade.
SkriptScripts of all lectures will be available.
Literatur"Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter.
KompetenzenKompetenzen
Fachspezifische KompetenzenKonzepte und Theoriengeprüft
Verfahren und Technologiengeprüft
Methodenspezifische KompetenzenAnalytische Kompetenzengefördert
Entscheidungsfindunggeprüft
Medien und digitale Technologiengefördert
Problemlösunggeprüft
Projektmanagementgefördert
Soziale KompetenzenKommunikationgefördert
Kooperation und Teamarbeitgeprüft
Kundenorientierunggefördert
Menschenführung und Verantwortunggefördert
Selbstdarstellung und soziale Einflussnahmegefördert
Sensibilität für Vielfalt gefördert
Verhandlunggefördert
Persönliche KompetenzenAnpassung und Flexibilitätgeprüft
Kreatives Denkengeprüft
Kritisches Denkengeprüft
Integrität und Arbeitsethikgefördert
Selbstbewusstsein und Selbstreflexion gefördert
Selbststeuerung und Selbstmanagement gefördert
151-0532-00LNonlinear Dynamics and Chaos IW4 KP2V + 2UG. Haller
KurzbeschreibungBasic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.
LernzielThis course is intended for Masters and Ph.D. students in engineering sciences, physics and applied mathematics who are interested in the behavior of nonlinear dynamical systems. It offers an introduction to the qualitative study of nonlinear physical phenomena modeled by differential equations or discrete maps. We discuss applications in classical mechanics, electrical engineering, fluid mechanics, and biology. A more advanced Part II of this class is offered every other year.
Inhalt(1) Basic facts about nonlinear systems: Existence, uniqueness, and dependence on initial data.

(2) Near equilibrium dynamics: Linear and Lyapunov stability

(3) Bifurcations of equilibria: Center manifolds, normal forms, and elementary bifurcations

(4) Nonlinear dynamical systems on the plane: Phase plane techniques, limit sets, and limit cycles.

(5) Time-dependent dynamical systems: Floquet theory, Poincare maps, averaging methods, resonance
SkriptThe class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture.
Voraussetzungen / Besonderes- Prerequisites: Analysis, linear algebra and a basic course in differential equations.

- Exam: two-hour written exam in English.

- Homework: A homework assignment will be due roughly every other week. Hints to solutions will be posted after the homework due dates.
151-0573-00LSystem Modeling Information W4 KP2V + 1UL. Guzzella
KurzbeschreibungEinführung in die Systemmodellierung für die Steuerung. Generische Modellierungsansätze auf der Grundlage erster Prinzipien, Lagrangealer Formalismus, Energieansätze und experimentelle Daten. Modellparametrierung und Parametrierung. Grundlegende Analyse von linearen und nichtlinearen Systemen.
LernzielErfahren Sie, wie man mathematisch ein physisches System oder einen Prozess in Form eines Modells beschreibt, das für Analyse- und Kontrollzwecke verwendbar ist.
InhaltDiese Vorlesung führt generische Systemmodellierungsansätze für steuerungsorientierte Modelle ein, die auf ersten Prinzipien und experimentellen Daten basieren. Die Vorlesung umfasst zahlreiche Beispiele für mechatronische, thermodynamische, chemische, flüssigkeitsdynamische, energie- und verfahrenstechnische Systeme. Modellskalierung, Linearisierung, Auftragsreduktion und Ausgleich. Parameterschätzung mit Methoden der kleinsten Quadrate. Verschiedene Fallstudien: Lautsprecher, Turbinen, Wasser angetriebene Rakete, geostationäre Satelliten usw. Die Übungen behandeln praktische Beispiele.
SkriptDas Skript in englischer Sprache wird in digitaler Form erhältlich sein.
LiteraturEine Literaturliste ist im Skript enthalten.
KompetenzenKompetenzen
Fachspezifische KompetenzenKonzepte und Theoriengeprüft
Verfahren und Technologiengeprüft
Methodenspezifische KompetenzenAnalytische Kompetenzengeprüft
Entscheidungsfindunggeprüft
Medien und digitale Technologiengefördert
Problemlösunggeprüft
Projektmanagementgefördert
Soziale KompetenzenKommunikationgefördert
Kooperation und Teamarbeitgefördert
Kundenorientierunggefördert
Menschenführung und Verantwortunggefördert
Selbstdarstellung und soziale Einflussnahmegefördert
Sensibilität für Vielfalt gefördert
Verhandlunggefördert
Persönliche KompetenzenAnpassung und Flexibilitätgefördert
Kreatives Denkengefördert
Kritisches Denkengefördert
Integrität und Arbeitsethikgefördert
Selbstbewusstsein und Selbstreflexion gefördert
Selbststeuerung und Selbstmanagement gefördert
151-0601-00LTheory of Robotics and Mechatronics Information
Findet dieses Semester nicht statt.
W4 KP3GNoch nicht bekannt
KurzbeschreibungThis course provides an introduction and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
LernzielRobotics is often viewed from three perspectives: perception (sensing), manipulation (affecting changes in the world), and cognition (intelligence). Robotic systems integrate aspects of all three of these areas. This course provides an introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
InhaltAn introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
Skriptavailable.
151-0563-01LDynamic Programming and Optimal Control Information W4 KP2V + 1UR. D'Andrea
KurzbeschreibungIntroduction to Dynamic Programming and Optimal Control.
LernzielCovers the fundamental concepts of Dynamic Programming & Optimal Control.
InhaltDynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
LiteraturDynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Voraussetzungen / BesonderesRequirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.
151-9905-00LApplied Compositional Thinking for Engineers II Information W4 KP3GA. Censi, J. Lorand
KurzbeschreibungThis course is an introduction to advanced topics in Applied Category Theory focused on the needs of applications. The course favors a computational, constructive, and compositional approach targeted to applications in engineering.
LernzielIn many domains of engineering and applied sciences, it would be beneficial to think explicitly about abstraction and compositionality, to improve both the understanding of problems and the design of solutions. Applied Category Theory is a field of mathematics that can help in thinking about precisely such topics. A problem, however, is that this type of mathematics is not traditionally taught -- to date, there exists no easy path for engineers to learn category theory that is approachable and emphasizes engineering applications. This course will fill this gap, extending the efforts proposed in the first part of the class (ACT4E I).

This course's goal is not to teach category theory for the sake of it, but to teach the "compositional way of thinking". Category theory will just be the means towards this end. This implies that the presentation of materials sometimes diverges from the usual way to teach category theory, and some common concepts might be de-emphasized in favor of more obscure concepts that are more useful for applications.

The applications shown in the class will be mainly in the domains of autonomous robotics and mobility.
InhaltCategories

Functors

Co-design problems

Natural transformations

Adjunctions

Traced monoidal categories

Computation:
- From mathematical models to algorithms
- Solving finite co-design problems
- Monads
- Modeling uncertainty

Enriched category theory:
- Profunctors
- Enriched categories
- Negative category theory

Operads

Linear logic and resources
SkriptSlides and notes will be provided.
LiteraturB. Fong, D.I. Spivak, Seven Sketches in Compositionality: An Invitation to Applied Category Theory (Link)
Censi, Lorand, Zardini, Applied Compositional Thinking for Engineers (Link)
Voraussetzungen / BesonderesThe course is self-contained and can be taken, in principle, without ACT4E I.

We assume this knowledge:
1) Basics of logic & mathematical thinking, ability to write simple mathematical proofs.
2) Basic algebra (sets, posets, relations, semigroups, groups).


Students who took 151-9904-00L Applied Compositional Thinking for Engineers I in the Spring Semester are sufficiently proficient in (1) and (2).
376-1219-00LRehabilitation Engineering II: Rehabilitation of Sensory and Vegetative FunctionsW3 KP2VR. Riener, O. Lambercy
KurzbeschreibungRehabilitation Engng is the application of science and technology to ameliorate the handicaps of individuals with disabilities to reintegrate them into society.The goal is to present classical and new rehabilitation engineering principles applied to compensate or enhance motor, sensory, and cognitive deficits. Focus is on the restoration and treatment of the human sensory and vegetative system.
LernzielProvide knowledge on the anatomy and physiology of the human sensory system, related dysfunctions and pathologies, and how rehabilitation engineering can provide sensory restoration and substitution.

This lecture is independent from Rehabilitation Engineering I. Thus, both lectures can be visited in arbitrary order.
InhaltIntroduction, problem definition, overview
Rehabilitation of visual function
- Anatomy and physiology of the visual sense
- Technical aids (glasses, sensor substitution)
- Retina and cortex implants
Rehabilitation of hearing function
- Anatomy and physiology of the auditory sense
- Hearing aids
- Cochlea Implants
Rehabilitation and use of kinesthetic and tactile function
- Anatomy and physiology of the kinesthetic and tactile sense
- Tactile/haptic displays for motion therapy (incl. electrical stimulation)
- Role of displays in motor learning
Rehabilitation of vestibular function
- Anatomy and physiology of the vestibular sense
- Rehabilitation strategies and devices (e.g. BrainPort)
Rehabilitation of vegetative Functions
- Cardiac Pacemaker
- Phrenic stimulation, artificial breathing aids
- Bladder stimulation, artificial sphincter
Brain stimulation and recording
- Deep brain stimulation for patients with Parkinson, epilepsy, depression
- Brain-Computer Interfaces
LiteraturIntroductory Books:

An Introduction to Rehabilitation Engineering. R. A. Cooper, H. Ohnabe, D. A. Hobson (Eds.). Taylor & Francis, 2007.

Principles of Neural Science. E. R. Kandel, J. H. Schwartz, T. M Jessell (Eds.). Mc Graw Hill, New York, 2000.

Force and Touch Feedback for Virtual Reality. G. C. Burdea (Ed.). Wiley, New York, 1996 (available on NEBIS).

Human Haptic Perception, Basics and Applications. M. Grunwald (Ed.). Birkhäuser, Basel, 2008.

The Sense of Touch and Its Rendering, Springer Tracts in Advanced Robotics 45, A. Bicchi et al.(Eds). Springer-Verlag Berlin, 2008.

Interaktive und autonome Systeme der Medizintechnik - Funktionswiederherstellung und Organersatz. Herausgeber: J. Werner, Oldenbourg Wissenschaftsverlag 2005.

Neural prostheses - replacing motor function after desease or disability. Eds.: R. Stein, H. Peckham, D. Popovic. New York and Oxford: Oxford University Press.

Advances in Rehabilitation Robotics - Human-Friendly Technologies on Movement Assistance and Restoration for People with Disabilities. Eds: Z.Z. Bien, D. Stefanov (Lecture Notes in Control and Information Science, No. 306). Springer Verlag Berlin 2004.

Intelligent Systems and Technologies in Rehabilitation Engineering. Eds: H.N.L. Teodorescu, L.C. Jain (International Series on Computational Intelligence). CRC Press Boca Raton, 2001.


Selected Journal Articles and Web Links:

Abbas, J., Riener, R. (2001) Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function. Neuromodulation 4, pp. 187-195.

Bach-y-Rita P., Tyler M., and Kaczmarek K (2003). Seeing with the brain. International journal of human-computer-interaction, 15(2):285-295.

Burdea, G., Popescu, V., Hentz, V., and Colbert, K. (2000): Virtual reality-based orthopedic telerehabilitation, IEEE Trans. Rehab. Eng., 8, pp. 430-432
Colombo, G., Jörg, M., Schreier, R., Dietz, V. (2000) Treadmill training of paraplegic patients using a robotic orthosis. Journal of Rehabilitation Research and Development, vol. 37, pp. 693-700.

Hayward, V. (2008): A Brief Taxonomy of Tactile Illusions and
Demonstrations That Can Be Done In a Hardware Store. Brain Research Bulletin, Vol 75, No 6, pp 742-752

Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T. (1998): Robot-aided neurorehabilitation, IEEE Trans. Rehab. Eng., 6, pp. 75-87

Levesque. V. (2005). Blindness, technology and haptics. Technical report, McGill University. Available at: Link

Quintern, J. (1998) Application of functional electrical stimulation in paraplegic patients. NeuroRehabilitation 10, pp. 205-250.

Riener, R., Nef, T., Colombo, G. (2005) Robot-aided neurorehabilitation for the upper extremities. Medical & Biological Engineering & Computing 43(1), pp. 2-10.

Riener, R. (1999) Model-based development of neuroprostheses for paraplegic patients. Royal Philosophical Transactions: Biological Sciences 354, pp. 877-894.

The vOICe. Link.

VideoTact, ForeThought Development, LLC. Link
Voraussetzungen / BesonderesTarget Group:
Students of higher semesters and PhD students of
- D-MAVT, D-ITET, D-INFK, D-HEST
- Biomedical Engineering, Robotics, Systems and Control
- Medical Faculty, University of Zurich
Students of other departments, faculties, courses are also welcome
This lecture is independent from Rehabilitation Engineering I. Thus, both lectures can be visited in arbitrary order.
401-0647-00LIntroduction to Mathematical OptimizationW5 KP2V + 1UD. Adjiashvili
KurzbeschreibungIntroduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering.
LernzielThe goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering.
InhaltTopics covered in this course include:
- Linear programming (simplex method, duality theory, shadow prices, ...).
- Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...).
- Modelling with mathematical optimization: applications of mathematical programming in engineering.
LiteraturInformation about relevant literature will be given in the lecture.
Voraussetzungen / BesonderesThis course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications.
401-3901-00LLinear & Combinatorial Optimization Information W11 KP4V + 2UR. Zenklusen
KurzbeschreibungMathematical treatment of optimization techniques for linear and combinatorial optimization problems.
LernzielThe goal of this course is to get a thorough understanding of various classical mathematical optimization techniques for linear and combinatorial optimization problems, with an emphasis on polyhedral approaches. In particular, we want students to develop a good understanding of some important problem classes in the field, of structural mathematical results linked to these problems, and of solution approaches based on such structural insights.
InhaltKey topics include:
- Linear programming and polyhedra;
- Flows and cuts;
- Combinatorial optimization problems and polyhedral techniques;
- Equivalence between optimization and separation.
Literatur- Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018.
- Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes.
- Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.
- Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986.
Voraussetzungen / BesonderesSolid background in linear algebra.

Former course title: Mathematical Optimization.
KompetenzenKompetenzen
Fachspezifische KompetenzenKonzepte und Theoriengeprüft
Verfahren und Technologiengefördert
Methodenspezifische KompetenzenAnalytische Kompetenzengeprüft
Entscheidungsfindunggeprüft
Medien und digitale Technologiengefördert
Problemlösunggeprüft
Projektmanagementgefördert
Soziale KompetenzenKommunikationgeprüft
Kooperation und Teamarbeitgefördert
Kundenorientierunggefördert
Menschenführung und Verantwortunggefördert
Selbstdarstellung und soziale Einflussnahmegefördert
Sensibilität für Vielfalt gefördert
Verhandlunggefördert
Persönliche KompetenzenAnpassung und Flexibilitätgefördert
Kreatives Denkengeprüft
Kritisches Denkengefördert
Integrität und Arbeitsethikgefördert
Selbstbewusstsein und Selbstreflexion gefördert
Selbststeuerung und Selbstmanagement gefördert
636-0007-00LComputational Systems Biology Information W6 KP3V + 2UJ. Stelling
KurzbeschreibungStudy of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification).
LernzielThe aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.
InhaltBiology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods.
SkriptLink
LiteraturU. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.

Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2010.

B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013
401-3055-64LAlgebraic Methods in Combinatorics Information
Findet dieses Semester nicht statt.
W6 KP2V + 1UB. Sudakov
KurzbeschreibungCombinatorics is a fundamental mathematical discipline as well as an essential component of many mathematical areas, and its study has experienced an impressive growth in recent years. This course provides a gentle introduction to Algebraic methods, illustrated by examples and focusing on basic ideas and connections to other areas.
LernzielThe students will get an overview of various algebraic methods for solving combinatorial problems. We expect them to understand the proof techniques and to use them autonomously on related problems.
InhaltCombinatorics is a fundamental mathematical discipline as well as an essential component of many mathematical areas, and its study has experienced an impressive growth in recent years. While in the past many of the basic combinatorial results were obtained mainly by ingenuity and detailed reasoning, the modern theory has grown out of this early stage and often relies on deep, well-developed tools.

One of the main general techniques that played a crucial role in the development of Combinatorics was the application of algebraic methods. The most fruitful such tool is the dimension argument. Roughly speaking, the method can be described as follows. In order to bound the cardinality of of a discrete structure A one maps its elements to vectors in a linear space, and shows that the set A is mapped to linearly independent vectors. It then follows that the cardinality of A is bounded by the dimension of the corresponding linear space. This simple idea is surprisingly powerful and has many famous applications.

This course provides a gentle introduction to Algebraic methods, illustrated by examples and focusing on basic ideas and connections to other areas. The topics covered in the class will include (but are not limited to):

Basic dimension arguments, Spaces of polynomials and tensor product methods, Eigenvalues of graphs and their application, the Combinatorial Nullstellensatz and the Chevalley-Warning theorem. Applications such as: Solution of Kakeya problem in finite fields, counterexample to Borsuk's conjecture, chromatic number of the unit distance graph of Euclidean space, explicit constructions of Ramsey graphs and many others.

The course website can be found at
Link
SkriptLectures will be on the blackboard only, but there will be a set of typeset lecture notes which follow the class closely.
Voraussetzungen / BesonderesStudents are expected to have a mathematical background and should be able to write rigorous proofs.
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