Suchergebnis: Katalogdaten im Herbstsemester 2018
Rechnergestützte Wissenschaften Bachelor | ||||||
Bachelor-Studium (Studienreglement 2012 und 2016) | ||||||
Grundlagenfächer | ||||||
Block G2 | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|
401-0647-00L | Introduction to Mathematical Optimization | O | 5 KP | 2V + 1U | D. Adjiashvili | |
Kurzbeschreibung | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Literatur | Information about relevant literature will be given in the lecture. | |||||
Voraussetzungen / Besonderes | 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. | |||||
Block G3 Die Lehrveranstaltungen von Block G3 finden im Frühjahrssemester statt. | ||||||
Block G4 Studierende, die aus einem anderen ETH-Studiengang in das zweite Studienjahr des Bachelor-Studiengangs RW übergetreten sind und deren Basisprüfung das Fach "Physik I" nicht umfasst, müssen im Prüfungsblock G4 anstelle von "Physik II" (im Frühjahrssemester) den Jahreskurs "Physik I und II" (402-0043-00L und 402-0044-00L) aus dem Bachelor-Studiengang Chemie belegen und die entsprechende Prüfung ablegen. | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
402-0043-00L | Physik I | W | 4 KP | 3V + 1U | J. Home | |
Kurzbeschreibung | Einführung in die Denk- und Arbeitsweise in der Physik unter Zuhilfenahme von Demonstrationsexperimenten: Mechanik von Massenpunkten und starren Körpern, Schwingungen und Wellen. | |||||
Lernziel | Vermittlung der physikalischen Denk- und Arbeitsweise und Einführung in die Methoden in einer experimentellen Wissenschaft. Die Studenten und Studentinnen soll lernen, physikalische Fragestellungen im eigenen Wissenschaftsbereich zu identifizieren, zu kommunizieren und zu lösen. | |||||
Inhalt | Mechanik (Bewegung, Newtonsche Axiome, Arbeit und Energie, Impulserhaltung, Drehbewegungen, Gravitation, deformierbare Körper) Schwingungen und Wellen (Schwingungen, mechanische Wellen, Akustik) | |||||
Skript | Die Vorlesung richtet sich nach dem Lehrbuch "Physik" von Paul A. Tipler. | |||||
Literatur | Tipler, Paul A., Mosca, Gene, Physik (für Wissenschaftler und Ingenieure), Springer Spektrum | |||||
Kernfächer | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
151-0107-20L | High Performance Computing for Science and Engineering (HPCSE) I | O | 4 KP | 4G | P. Koumoutsakos | |
Kurzbeschreibung | This course gives an introduction into algorithms and numerical methods for parallel computing for multi and many-core architectures and for applications from problems in science and engineering. | |||||
Lernziel | Introduction to HPC for scientists and engineers Fundamental of: 1. Parallel Computing Architectures 2. MultiCores 3. ManyCores | |||||
Inhalt | Parallel Programming models and languages (OpenMP, MPI). Parallel Performance metrics and Code Optimization. Examples based on grid and particle methods for solving Partial Differential Equations and on fundamentals of stochastic optimisation and machine learning. | |||||
Skript | Link Class notes, handouts | |||||
Bachelor-Arbeit Wenn Sie anstelle von 401-2000-00L Scientific Works in Mathematics die Lerneinheit 402-2000-00L Scientific Works in Physics anrechnen lassen möchten (dies ist erlaubt im Studiengang Rechnergestützte Wissenschaften), so wenden Sie sich nach dem Verfügen des Resultates an das Studiensekretariat (Link). | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
401-2000-00L | Scientific Works in Mathematics Zielpublikum: Bachelor-Studierende im dritten Jahr; Master-Studierende, welche noch keine entsprechende Ausbildung vorweisen können. | O | 0 KP | E. Kowalski | ||
Kurzbeschreibung | Introduction to scientific writing for students with focus on publication standards and ethical issues, especially in the case of citations (references to works of others.) | |||||
Lernziel | Learn the basic standards of scientific works in mathematics. | |||||
Inhalt | - Types of mathematical works - Publication standards in pure and applied mathematics - Data handling - Ethical issues - Citation guidelines | |||||
Skript | Moodle of the Mathematics Library: Link | |||||
Voraussetzungen / Besonderes | Weisung Link | |||||
401-2000-01L | Recherchieren in der Mathematik [wird überarbeitet] Für Details und zur Registrierung für den freiwilligen MathBib-Schulungskurs: Link | Z | 0 KP | Referent/innen | ||
Kurzbeschreibung | Freiwilliger Kurs "Recherchieren in der Mathematik" angeboten von der Mathematikbibliothek. | |||||
Lernziel | ||||||
402-2000-00L | Scientific Works in Physics Zielpublikum: Master-Studierende, welche noch keine entsprechende Ausbildung vorweisen können. Weisung Link | W | 0 KP | C. Grab | ||
Kurzbeschreibung | Literature Review: ETH-Library, Journals in Physics, Google Scholar; Thesis Structure: The IMRAD Model; Document Processing: LaTeX and BibTeX, Mathematical Writing, AVETH Survival Guide; ETH Guidelines for Integrity; Authorship Guidelines; ETH Citation Etiquettes; Declaration of Originality. | |||||
Lernziel | Basic standards for scientific works in physics: How to write a Master Thesis. What to know about research integrity. | |||||
401-3990-01L | Bachelor-Arbeit Nur für Rechnergestützte Wissenschaften BSc, Studienreglement 2012 und 2016. Voraussetzung: erfolgreicher Abschluss der Lerneinheit 401-2000-00L Scientific Works in Mathematics oder 402-2000-00L Scientific Works in Physics Weitere Angaben unter Link | O | 8 KP | 11D | Betreuer/innen | |
Kurzbeschreibung | Die Bachelor-Arbeit bildet den Abschluss des Studiengangs. Sie soll einerseits dazu dienen, das Wissen in einem bestimmten Fachgebiet zu vertiefen sowie in einen ersten Kontakt mit Anwendungen zu kommen und Probleme aus solchen Anwendungen in einer bestehenden wissenschaftlichen Gruppe rechnergestützt anzugehen. Die Bachelor-Arbeit umfasst ca. 160 Stunden. | |||||
Lernziel | Die Bachelorarbeit soll einerseits dazu dienen, das Wissen in einem bestimmten Fachgebiet zu vertiefen sowie in einen ersten Kontakt mit Anwendungen zu kommen und Probleme aus solchen Anwendungen rechnergestützt anzugehen. Andererseits soll auch gelernt werden, in einer bestehenden wissenschaftlichen Gruppe mitzuarbeiten. | |||||
Voraussetzungen / Besonderes | Der verantwortliche Leiter der Bachelorarbeit definiert die Aufgabenstellung und legt den Beginn der Bachelorarbeit und den Abgabetermin fest. Die Bachelorarbeit wird mit einem schriftlichen Bericht abgeschlossen. Die Leistung wird mit einer Note bewertet. | |||||
Für alle Studienreglemente | ||||||
Vertiefungsgebiete | ||||||
Astrophysik | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
401-7851-00L | Theoretical Astrophysics (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: AST512 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 10 KP | 4V + 2U | R. Teyssier | |
Kurzbeschreibung | This course covers the foundations of astrophysical fluid dynamics, the Boltzmann equation, equilibrium systems and their stability, the structure of stars, astrophysical turbulence, accretion disks and their stability, the foundations of radiative transfer, collisionless systems, the structure and stability of dark matter halos and galactic disks. | |||||
Lernziel | ||||||
Literatur | Course Materials: 1- The Physics of Astrophysics, Volume 1: Radiation by Frank H. Shu 2- The Physics of Astrophysics, Volume 2: Gas Dynamics by Frank H. Shu 3- Foundations of radiation hydrodynamics, Dimitri Mihalas and Barbara Weibel-Mihalas 4- Radiative Processes in Astrophysics, George B. Rybicki and Alan P. Lightman 5- Galactic Dynamics, James Binney and Scott Tremaine | |||||
Voraussetzungen / Besonderes | Prerequisites: Introduction to Astrophysics Mathematical Methods for the Physicist Quantum Mechanics (All preferred but not obligatory) Prior Knowledge: Mechanics Quantum Mechanics and atomic physics Thermodynamics Fluid Dynamics Electrodynamics | |||||
401-7855-00L | Computational Astrophysics (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: AST245 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 6 KP | 2V | L. M. Mayer | |
Kurzbeschreibung | ||||||
Lernziel | Acquire knowledge of main methodologies for computer-based models of astrophysical systems,the physical equations behind them, and train such knowledge with simple examples of computer programmes | |||||
Inhalt | 1. Integration of ODE, Hamiltonians and Symplectic integration techniques, time adaptivity, time reversibility 2. Large-N gravity calculation, collisionless N-body systems and their simulation 3. Fast Fourier Transform and spectral methods in general 4. Eulerian Hydrodynamics: Upwinding, Riemann solvers, Limiters 5. Lagrangian Hydrodynamics: The SPH method 6. Resolution and instabilities in Hydrodynamics 7. Initial Conditions: Cosmological Simulations and Astrophysical Disks 8. Physical Approximations and Methods for Radiative Transfer in Astrophysics | |||||
Literatur | Galactic Dynamics (Binney & Tremaine, Princeton University Press), Computer Simulation using Particles (Hockney & Eastwood CRC press), Targeted journal reviews on computational methods for astrophysical fluids (SPH, AMR, moving mesh) | |||||
Voraussetzungen / Besonderes | Some knowledge of UNIX, scripting languages (see Link as an example), some prior experience programming, knowledge of C, C++ beneficial | |||||
Atmosphärenphysik | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
701-0023-00L | Atmosphäre | W | 3 KP | 2V | E. Fischer, T. Peter | |
Kurzbeschreibung | Grundlagen der Atmosphäre, physikalischer Aufbau und chemische Zusammensetzung, Spurengase, Kreisläufe in der Atmosphäre, Zirkulation, Stabilität, Strahlung, Kondensation, Wolken, Oxidationspotential und Ozonschicht. | |||||
Lernziel | Verständnis grundlegender physikalischer und chemischer Prozesse in der Atmosphäre. Kenntnis über die Mechanismen und Zusammenhänge von: Wetter - Klima, Atmosphäre - Ozeane - Kontinente, Troposphäre - Stratosphäre. Verständnis von umweltrelevanten Strukturen und Vorgängen in sehr unterschiedlichem Massstab. Grundlagen für eine modellmässige Darstellung komplexer Zusammenhänge in der Atmosphäre. | |||||
Inhalt | Grundlagen der Atmosphäre, physikalischer Aufbau und chemische Zusammensetzung, Spurengase, Kreisläufe in der Atmosphäre, Zirkulation, Stabilität, Strahlung, Kondensation, Wolken, Oxidationspotential und Ozonschicht. | |||||
Skript | Schriftliche Unterlagen werden abgegeben. | |||||
Literatur | - John H. Seinfeld and Spyros N. Pandis, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley, New York, 1998. - Gösta H. Liljequist, Allgemeine Meteorologie, Vieweg, Braunschweig, 1974. | |||||
Chemie | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
529-0004-01L | Computer Simulation in Chemistry, Biology and Physics | W | 6 KP | 4G | P. H. Hünenberger | |
Kurzbeschreibung | Molecular models, Force fields, Boundary conditions, Electrostatic interactions, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. | |||||
Lernziel | Introduction to computer simulation of (bio)molecular systems, development of skills to carry out and interpret computer simulations of biomolecular systems. | |||||
Inhalt | Molecular models, Force fields, Spatial boundary conditions, Calculation of Coulomb forces, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. | |||||
Skript | Available (copies of powerpoint slides distributed before each lecture) | |||||
Literatur | See: Link | |||||
Voraussetzungen / Besonderes | Since the exercises on the computer do convey and test essentially different skills as those being conveyed during the lectures and tested at the oral exam, the results of the exercises are taken into account when evaluating the results of the exam (learning component, possible bonus of up to 0.25 points on the exam mark). For more information about the lecture: Link | |||||
Fluiddynamik | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
151-0103-00L | Fluiddynamik II | W | 3 KP | 2V + 1U | P. Jenny | |
Kurzbeschreibung | Ebene Potentialströmungen: Stromfunktion und Potential, Singularitätenmethode, instationäre Strömung, aerodynamische Begriffe. Drehungsbehaftete Strömungen: Wirbelstärke und Zirkulation, Wirbeltransportgleichung, Wirbelsätze von Helmholtz und Kelvin. Kompressible Strömungen: Stromfadentheorie, senkrechter und schiefer Verdichtungsstoss, Laval-Düse, Prandtl-Meyer-Expansion, Reibungseinfluss. | |||||
Lernziel | Erweiterung der Grundlagen der Fluiddynamik. Grundbegriffe, Phänomene und Gesetzmässigkeiten von drehungsfreien, drehungsbehafteten und eindimensionalen kompressiblen Strömungen vermitteln. | |||||
Inhalt | Ebene Potentialströmungen: Stromfunktion und Potential, komplexe Darstellung, Singularitätenmethode, instationäre Strömung, aerodynamische Begriffe. Drehungsbehaftete Strömungen: Wirbelstärke und Zirkulation, Wirbeldynamik und Wirbeltransportgleichung, Wirbelsätze von Helmholtz und Kelvin. Kompressible Strömungen: Stromfadentheorie, senkrechter und schiefer Verdichtungsstoss, Laval-Düse, Prandtl-Meyer-Expansion, Reibungseinfluss. | |||||
Skript | ja (Siehe auch untenstehende Information betreffend der Literatur.) | |||||
Literatur | P.K. Kundu, I.M. Cohen, D.R. Dowling: Fluid Mechanics, Academic Press, 5th ed., 2011 (includes a free copy of the DVD "Multimedia Fluid Mechanics") P.K. Kundu, I.M. Cohen, D.R. Dowling: Fluid Mechanics, Academic Press, 6th ed., 2015 (does NOT include a free copy of the DVD "Multimedia Fluid Mechanics") | |||||
Voraussetzungen / Besonderes | Analysis I/II, Fluiddynamik I, Grundbegriffe der Thermodynamik (Thermodynamik I). Für die Formulierung der Grundlagen der Fluiddynamik werden unabdingbar Begriffe und Ergebnisse aus der Mathematik benötigt. Erfahrungsgemäss haben einige Studierende damit Schwierigkeiten. Es wird daher dringend empfohlen, insbesondere den Stoff über - elementare Funktionen (wie sin, cos, tan, exp, deren Umkehrfunktionen, Ableitungen und Integrale) sowie über - Vektoranalysis (Gradient, Divergenz, Rotation, Linienintegral ("Arbeit"), Integralsätze von Gauss und von Stokes, Potentialfelder als Lösungen der Laplace-Gleichung) zu wiederholen. Ferner wird der Umgang mit - komplexen Zahlen und Funktionen (siehe Anhang des Skripts Analysis I/II Teil C und Zusammenfassung im Anhang C des Skripts Fluiddynamik) benötigt. Literatur z.B.: U. Stammbach: Analysis I/II, Skript Teile A, B und C. | |||||
Systems and Control | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
227-0103-00L | Regelsysteme | W | 6 KP | 2V + 2U | F. Dörfler | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Literatur | 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. | |||||
Voraussetzungen / Besonderes | Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation. | |||||
227-0045-00L | Signal- und Systemtheorie I | W | 4 KP | 2V + 2U | H. Bölcskei | |
Kurzbeschreibung | Signaltheorie und Systemtheorie (zeitkontinuierlich und zeitdiskret): Signalanalyse im Zeit- und Frequenzbereich, Signalräume, Hilberträume, verallgemeinerte Funktionen, lineare zeitinvariante Systeme, Abtasttheoreme, zeitdiskrete Signale und Systeme, digitale Filterstrukturen, diskrete Fourier-Transformation (DFT), endlich-dimensionale Signale und Systeme, schnelle Fouriertransformation (FFT). | |||||
Lernziel | Einführung in die mathematische Signaltheorie und Systemtheorie. | |||||
Inhalt | Signaltheorie und Systemtheorie (zeitkontinuierlich und zeitdiskret): Signalanalyse im Zeit- und Frequenzbereich, Signalräume, Hilberträume, verallgemeinerte Funktionen, lineare zeitinvariante Systeme, Abtasttheoreme, zeitdiskrete Signale und Systeme, digitale Filterstrukturen, diskrete Fourier-Transformation (DFT), endlich-dimensionale Signale und Systeme, schnelle Fouriertransformation (FFT). | |||||
Skript | Vorlesungsskriptum, Übungsskriptum mit Lösungen. | |||||
Robotik | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
151-0601-00L | Theory of Robotics and Mechatronics | W | 4 KP | 3G | P. Korba, S. Stoeter | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Skript | available. | |||||
252-0535-00L | Advanced Machine Learning | W | 8 KP | 3V + 2U + 2A | J. M. Buhmann | |
Kurzbeschreibung | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | |||||
Lernziel | Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data. | |||||
Inhalt | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems | |||||
Skript | No lecture notes, but slides will be made available on the course webpage. | |||||
Literatur | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | |||||
Voraussetzungen / Besonderes | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. | |||||
263-3210-00L | Deep Learning Maximale Teilnehmerzahl: 300 | W | 4 KP | 2V + 1U | F. Perez Cruz | |
Kurzbeschreibung | Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations. | |||||
Lernziel | In recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology. | |||||
Voraussetzungen / Besonderes | This is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit. The participation in the course is subject to the following conditions: 1) The number of participants is limited to 300 students (MSc and PhDs). 2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below: Machine Learning Link Computational Intelligence Lab Link Learning and Intelligent Systems/Introduction to Machine Learning Link Statistical Learning Theory Link Computational Statistics Link Probabilistic Artificial Intelligence Link Data Mining: Learning from Large Data Sets Link | |||||
263-5902-00L | Computer Vision | W | 6 KP | 3V + 1U + 1A | M. Pollefeys, V. Ferrari, L. Van Gool | |
Kurzbeschreibung | The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises. | |||||
Lernziel | The objectives of this course are: 1. To introduce the fundamental problems of computer vision. 2. To introduce the main concepts and techniques used to solve those. 3. To enable participants to implement solutions for reasonably complex problems. 4. To enable participants to make sense of the computer vision literature. | |||||
Inhalt | Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition | |||||
Voraussetzungen / Besonderes | It is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course. | |||||
151-0563-01L | Dynamic Programming and Optimal Control | W | 4 KP | 2V + 1U | R. D'Andrea | |
Kurzbeschreibung | Introduction to Dynamic Programming and Optimal Control. | |||||
Lernziel | Covers the fundamental concepts of Dynamic Programming & Optimal Control. | |||||
Inhalt | Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control. | |||||
Literatur | Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover. | |||||
Voraussetzungen / Besonderes | Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra. | |||||
151-0851-00L | Robot Dynamics | W | 4 KP | 2V + 2U | M. Hutter, R. Siegwart | |
Kurzbeschreibung | We will provide an overview on how to kinematically and dynamically model typical robotic systems such as robot arms, legged robots, rotary wing systems, or fixed wing. | |||||
Lernziel | The primary objective of this course is that the student deepens an applied understanding of how to model the most common robotic systems. The student receives a solid background in kinematics, dynamics, and rotations of multi-body systems. On the basis of state of the art applications, he/she will learn all necessary tools to work in the field of design or control of robotic systems. | |||||
Inhalt | The course consists of three parts: First, we will refresh and deepen the student's knowledge in kinematics, dynamics, and rotations of multi-body systems. In this context, the learning material will build upon the courses for mechanics and dynamics available at ETH, with the particular focus on their application to robotic systems. The goal is to foster the conceptual understanding of similarities and differences among the various types of robots. In the second part, we will apply the learned material to classical robotic arms as well as legged systems and discuss kinematic constraints and interaction forces. In the third part, focus is put on modeling fixed wing aircraft, along with related design and control concepts. In this context, we also touch aerodynamics and flight mechanics to an extent typically required in robotics. The last part finally covers different helicopter types, with a focus on quadrotors and the coaxial configuration which we see today in many UAV applications. Case studies on all main topics provide the link to real applications and to the state of the art in robotics. | |||||
Voraussetzungen / Besonderes | The contents of the following ETH Bachelor lectures or equivalent are assumed to be known: Mechanics and Dynamics, Control, Basics in Fluid Dynamics. |
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