# Search result: Catalogue data in Autumn Semester 2018

Electrical Engineering and Information Technology Master | ||||||

Master Studies (Programme Regulations 2018) | ||||||

Systems and Control The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Systems and Control", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html. The individual study plan is subject to the tutor's approval. | ||||||

Core Courses These core courses are particularly recommended for the field of "Systems and Control". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT. | ||||||

Foundation Core Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|---|

227-0103-00L | Control Systems | W | 6 credits | 2V + 2U | F. Dörfler | |

Abstract | Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems. | |||||

Learning objective | Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems. | |||||

Content | Process automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation. | |||||

Literature | K. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010. R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007. G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010. J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014. J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014. | |||||

Prerequisites / Notice | Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation. | |||||

Advanced Core Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

227-0225-00L | Linear System Theory | W | 6 credits | 5G | M. Kamgarpour | |

Abstract | The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems. | |||||

Learning objective | Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems. | |||||

Content | - Proof techniques and practices. - Linear spaces, normed linear spaces and Hilbert spaces. - Ordinary differential equations, existence and uniqueness of solutions. - Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case. - Controllability and observability, duality. Time invariant systems treated as a special case. - Stability and stabilization, observers, state and output feedback, separation principle. | |||||

Lecture notes | Available on the course Moodle platform. | |||||

Prerequisites / Notice | Sufficient mathematical maturity with special focus on logic, linear algebra, analysis. | |||||

227-0697-00L | Industrial Process Control | W | 4 credits | 3G | M. Mercangöz, A. Horch | |

Abstract | Introduction to process automation and its application in process industry and power generation | |||||

Learning objective | Knowledge of process automation and its application in industry and power generation | |||||

Content | Introduction to process automation: system architecture, data handling, communication (fieldbusses), process visualization, engineering, etc. Analysis and design of open loop control problems: discrete automata, decision tables, petri-nets, drive control and object oriented function group automation philosophy, RT-UML. Engineering: Application programming in IEC61131-3 (function blocks, sequence control, structured text); process visualization and operation; engineering integration from sensor, cabling, topology design, function, visualization, diagnosis, to documentation; Industry standards (e.g. OPC, Profibus); Ergonomic design, safety (IEC61508) and availability, supervision and diagnosis. Practical examples from process industry, power generation and newspaper production. | |||||

Lecture notes | Slides will be available as .PDF documents, see "Learning materials" (for registered students only) | |||||

Prerequisites / Notice | Exercises: Tuesday 15-16 Practical exercises will illustrate some topics, e.g. some control software coding using industry standard programming tools based on IEC61131-3. | |||||

151-0563-01L | Dynamic Programming and Optimal Control | W | 4 credits | 2V + 1U | R. D'Andrea | |

Abstract | Introduction to Dynamic Programming and Optimal Control. | |||||

Learning objective | Covers the fundamental concepts of Dynamic Programming & Optimal Control. | |||||

Content | Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control. | |||||

Literature | Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover. | |||||

Prerequisites / Notice | Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra. | |||||

Specialization Courses These specialization courses are particularly recommended for the area of "Systems and Control", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialization courses during the Master's Programme. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

227-0102-00L | Discrete Event Systems | W | 6 credits | 4G | L. Thiele, L. Vanbever, R. Wattenhofer | |

Abstract | Introduction to discrete event systems. We start out by studying popular models of discrete event systems. In the second part of the course we analyze discrete event systems from an average-case and from a worst-case perspective. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, Worst-Case Event Systems, Verification, Network Calculus. | |||||

Learning objective | Over 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. | |||||

Content | 1. Introduction 2. Automata and Languages 3. Smarter Automata 4. Specification Models 5. Stochastic Discrete Event Systems 6. Worst-Case Event Systems 7. Network Calculus | |||||

Lecture notes | Available | |||||

Literature | [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 | |||||

227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |

Abstract | Light 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. | |||||

Learning objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||

Content | This 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. | |||||

Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||

Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | |||||

227-0526-00L | Power System Analysis | W | 6 credits | 4G | G. Hug | |

Abstract | The goal of this course is understanding the stationary and dynamic problems in electrical power systems. The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power networks. | |||||

Learning objective | The goal of this course is understanding the stationary and dynamic problems in electrical power systems and the application of analysis tools in steady and dynamic states. | |||||

Content | The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power grids. Approaches such as the Newton-Raphson algorithm applied to power flow equations, superposition technique for short-circuit analysis, equal area criterion and nose curve analysis are discussed as well as power flow computation techniques for distribution grids. | |||||

Lecture notes | Lecture notes. | |||||

227-0689-00L | System Identification | W | 4 credits | 2V + 1U | R. Smith | |

Abstract | Theory and techniques for the identification of dynamic models from experimentally obtained system input-output data. | |||||

Learning objective | To 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. | |||||

Content | Introduction 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. | |||||

Literature | "System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999. "Dynamic system identification: Experimental design and data analysis", GC Goodwin and RL Payne, Academic Press, 1977. | |||||

Prerequisites / Notice | Control systems (227-0216-00L) or equivalent. | |||||

227-0945-00L | Cell and Molecular Biology for Engineers IThis course is part I of a two-semester course. | W | 3 credits | 2G | C. Frei | |

Abstract | The 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. | |||||

Learning objective | After 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. | |||||

Content | Lectures will include the following topics (part I and II): DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development 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. | |||||

Lecture notes | Scripts of all lectures will be available. | |||||

Literature | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter. | |||||

151-0532-00L | Nonlinear Dynamics and Chaos I | W | 4 credits | 2V + 2U | F. Kogelbauer | |

Abstract | Basic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics. | |||||

Learning objective | This 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. | |||||

Content | (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 | |||||

Lecture notes | The class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture. | |||||

Prerequisites / Notice | - 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. | |||||

151-0573-00L | System Modeling | W | 4 credits | 2V + 2U | G. Ducard | |

Abstract | Introduction to system modeling for control. Generic modeling approaches based on first principles, Lagrangian formalism, energy approaches and experimental data. Model parametrization and parameter estimation. Basic analysis of linear and nonlinear systems. | |||||

Learning objective | Learn how to mathematically describe a physical system or a process in the form of a model usable for analysis and control purposes. | |||||

Content | This class introduces generic system-modeling approaches for control-oriented models based on first principles and experimental data. The class will span numerous examples related to mechatronic, thermodynamic, chemistry, fluid dynamic, energy, and process engineering systems. Model scaling, linearization, order reduction, and balancing. Parameter estimation with least-squares methods. Various case studies: loud-speaker, turbines, water-propelled rocket, geostationary satellites, etc. The exercises address practical examples. | |||||

Lecture notes | The handouts in English will be sold in the first lecture. | |||||

Literature | A list of references is included in the handouts. | |||||

151-0601-00L | Theory of Robotics and Mechatronics | W | 4 credits | 3G | P. Korba, S. Stoeter | |

Abstract | This 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. It’s a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems. | |||||

Learning objective | Robotics 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. This course is a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems. | |||||

Content | 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. | |||||

Lecture notes | available. | |||||

151-0563-01L | Dynamic Programming and Optimal Control | W | 4 credits | 2V + 1U | R. D'Andrea | |

Abstract | Introduction to Dynamic Programming and Optimal Control. | |||||

Learning objective | Covers the fundamental concepts of Dynamic Programming & Optimal Control. | |||||

Content | Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control. | |||||

Literature | Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover. | |||||

Prerequisites / Notice | Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra. | |||||

376-1219-00L | Rehabilitation Engineering II: Rehabilitation of Sensory and Vegetative Functions | W | 3 credits | 2V | R. Riener, R. Gassert, O. Lambercy | |

Abstract | Rehabilitation 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. | |||||

Learning objective | Provide 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. | |||||

Content | Introduction, 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 | |||||

Literature | Introductory 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: http://www.cim.mcgill.ca/~vleves/docs/VL-CIM-TR-05.08.pdf 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. http://www.seeingwithsound.com. VideoTact, ForeThought Development, LLC. http://my.execpc.com/?dwysocki/videotac.html | |||||

Prerequisites / Notice | Target 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. | |||||

401-0647-00L | Introduction to Mathematical Optimization | W | 5 credits | 2V + 1U | D. Adjiashvili | |

Abstract | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||

Learning objective | The 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. | |||||

Content | Topics 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. | |||||

Literature | Information about relevant literature will be given in the lecture. | |||||

Prerequisites / Notice | This 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. | |||||

401-3901-00L | Mathematical Optimization | W | 11 credits | 4V + 2U | R. Weismantel | |

Abstract | Mathematical treatment of diverse optimization techniques. | |||||

Learning objective | Advanced optimization theory and algorithms. | |||||

Content | 1) Linear optimization: The geometry of linear programming, the simplex method for solving linear programming problems, Farkas' Lemma and infeasibility certificates, duality theory of linear programming. 2) Nonlinear optimization: Lagrange relaxation techniques, Newton method and gradient schemes for convex optimization. 3) Integer optimization: Ties between linear and integer optimization, total unimodularity, complexity theory, cutting plane theory. 4) Combinatorial optimization: Network flow problems, structural results and algorithms for matroids, matchings, and, more generally, independence systems. | |||||

Literature | 1) D. Bertsimas & R. Weismantel, "Optimization over Integers". Dynamic Ideas, 2005. 2) A. Schrijver, "Theory of Linear and Integer Programming". John Wiley, 1986. 3) D. Bertsimas & J.N. Tsitsiklis, "Introduction to Linear Optimization". Athena Scientific, 1997. 4) Y. Nesterov, "Introductory Lectures on Convex Optimization: a Basic Course". Kluwer Academic Publishers, 2003. 5) C.H. Papadimitriou, "Combinatorial Optimization". Prentice-Hall Inc., 1982. | |||||

Prerequisites / Notice | Linear algebra. | |||||

636-0007-00L | Computational Systems Biology | W | 6 credits | 3V + 2U | J. Stelling | |

Abstract | Study 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). | |||||

Learning objective | The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks. | |||||

Content | Biology 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. | |||||

Lecture notes | http://www.csb.ethz.ch/education/lectures.html | |||||

Literature | U. 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 |

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