Search result: Catalogue data in Autumn Semester 2017
|Computer Science Bachelor|
|Bachelor Studies (Programme Regulations 2016)|
|First Year Examinations|
|First Year Examination Block 1|
|401-0131-00L||Linear Algebra||O||7 credits||4V + 2U||Ö. Imamoglu, O. Sorkine Hornung|
|Abstract||Introduction to linear algebra (vector spaces, linear transformations, matrices) , matrix decompositions (LU, QR, eigenvalue, and singular value decomposition).|
|Objective||Die Lernziele sind:|
- die fundamentalen Konzepte der linearen Algebra gut zu verstehen
- Anwendungen der linearen Algebra in der Informatik kennenzulernen
Linear systems of equations, vectors and matrices, norms and scalar products, LU decomposition, vector spaces and linear transformations, least squares problems, QR decomposition, determinants, eigenvalues and eigenvectors, singular value decomposition, applications.
|Lecture notes||Lecture notes "Linear Algebra" (Gutknecht) in German, with English expressions for all technical terms.|
|Prerequisites / Notice||The relevant high school material is reviewed briefly at the beginning.|
|252-0025-00L||Discrete Mathematics||O||7 credits||4V + 2U||U. Maurer|
|Abstract||Content: Mathematical reasoning and proofs, abstraction. Sets, relations (e.g. equivalence and order relations), functions, (un-)countability, number theory, algebra (groups, rings, fields, polynomials, subalgebras, morphisms), logic (propositional and predicate logic, proof calculi).|
|Objective||The primary goals of this course are (1) to introduce the most important concepts of discrete mathematics, (2) to understand and appreciate the role of abstraction and mathematical proofs, and (3) to discuss a number of applications, e.g. in cryptography, coding theory, and algorithm theory.|
|Content||See course description.|
|Lecture notes||available (in english)|
|252-0027-00L||Introduction to Programming||O||7 credits||4V + 2U||T. Gross|
|Abstract||Introduction to fundamental concepts of modern programming and operational skills for developing high-quality programs, including large programs as in industry. The course introduces software engineering principles with an object-oriented approach based.|
|Objective||Many people can write programs. The "Introduction to Programming" course goes beyond that basic goal: it teaches the fundamental concepts and skills necessary to perform programming at a professional level. As a result of successfully completing the course, students master the fundamental control structures, data structures, reasoning patterns and programming language mechanisms characterizing modern programming, as well as the fundamental rules of producing high-quality software. They have the necessary programming background for later courses introducing programming skills in specialized application areas.|
|Content||Basics of object-oriented programming. Objects and classes. Pre- and postconditions, class invariants, Design by Contract. Fundamental control structures. Assignment and References. Basic hardware concepts. Fundamental data structures and algorithms. Recursion. Inheritance and interfaces, introduction to event-driven design and concurrent programming. Basic concepts of Software Engineering such as the software process, specification and documentation, reuse and quality assurance.|
|Lecture notes||The lecture slides are available for download on the course page.|
|Literature||See the course page for up-to-date information.|
|Prerequisites / Notice||There are no special prerequisites. Students are expected to enroll in the other courses offered to first-year students of computer science.|
|252-0026-00L||Algorithms and Data Structures||O||7 credits||3V + 2U + 1A||P. Widmayer, M. Püschel, D. Steurer|
|Abstract||This course is about fundamental algorithm design paradigms, classic algorithmic problems, and data structures. The connection between algorithms and data structures is explained for geometric and graph problems. For this purpose, fundamental graph theoretic concepts are introduced.|
|Objective||An understanding of the design and analysis of fundamental algorithms and data structures.|
|Content||Es werden grundlegende Algorithmen und Datenstrukturen vorgestellt und analysiert. Dazu gehören auf der einen Seite Entwurfsmuster für Algorithmen, wie Induktion, divide-and-conquer, backtracking und dynamische Optimierung, ebenso wie klassische algorithmische Probleme, wie Suchen und Sortieren. Auf der anderen Seite werden Datenstrukturen für verschiedene Zwecke behandelt, darunter verkettete Listen, Hashtabellen, balancierte Suchbäume, verschiedene heaps und union-find-Strukturen. Weiterhin wird Adaptivität bei Datenstrukturen (wie etwa Splay-Bäume) und bei Algorithmen (wie etwa online-Algorithmen) beleuchtet. Das Zusammenspiel von Algorithmen und Datenstrukturen wird anhand von Geometrie- und Graphenproblemen illustriert. Hierfür werden grundlegende Konzepte der Graphentheorie eingeführt.|
|Literature||Th. Ottmann, P.Widmayer: Algorithmen und Datenstrukturen, Spektrum-Verlag, 5. Auflage, Heidelberg, Berlin, Oxford, 2011|
| First Year Examination Block 2|
Offered in the spring semester.
|252-0057-00L||Theoretical Computer Science |
Remark: Students, who already took the course 252-0065-00 Theoretische Informatik (8 KP) are not allowed to register for 252-0057-00 Theoretische Informatik (7 KP).
|O||7 credits||4V + 2U||J. Hromkovic|
|Abstract||Concepts to cope with: a) what can be accomplished in a fully automated fashion (algorithmically solvable) b) How to measure the inherent difficulty of tasks (problems) c) What is randomness and how can it be useful? d) What is nondeterminism and what role does it play in CS? e) How to represent infinite objects by finite automata and grammars?|
|Objective||Learning the basic concepts of computer science along their historical development|
|Content||This lecture gives an introduction to theoretical computer science, presenting the basic concepts and methods of computer science in its historical context. We present computer science as an interdisciplinary science which, on the one hand, investigates the border between the possible and the impossible and the quantitative laws of information processing, and, on the other hand, designs, analyzes, verifies, and implements computer systems.|
The main topics of the lecture are:
- alphabets, words, languages, measuring the information content of words, representation of algorithmic tasks
- finite automata, regular and context-free grammars
- Turing machines and computability
- complexity theory and NP-completeness
- design of algorithms for hard problems
|Lecture notes||The lecture is covered in detail by the textbook "Theoretical Computer Science".|
1. J. Hromkovic: Theoretische Informatik. 5th edition, Springer Vieweg 2014.
2. J. Hromkovic: Theoretical Computer Science. Springer 2004.
3. M. Sipser: Introduction to the Theory of Computation, PWS Publ. Comp.1997
4. J.E. Hopcroft, R. Motwani, J.D. Ullman: Introduction to Automata Theory, Languages, and Computation (3rd Edition), Addison-Wesley 2006.
5. I. Wegener: Theoretische Informatik. Teubner.
More exercises and examples in:
6. A. Asteroth, Ch. Baier: Theoretische Informatik
|Prerequisites / Notice||During the semester, two non-obligatory test exams will be offered.|
|252-0061-00L||Systems Programming and Computer Architecture |
Remark: Students, who already took the course 252-0066-00 Systems Programming and Computer Architecture (8KP) are not allowed to register for 252-0061-00 Systems Programming and Computer Architecture (7 KP).
|O||7 credits||4V + 2U||T. Roscoe|
|Abstract||Introduction to systems programming. C and assembly language,|
floating point arithmetic, basic translation of C into assembler,
compiler optimizations, manual optimizations. How hardware features
like superscalar architecture, exceptions and interrupts, caches,
virtual memory, multicore processors, devices, and memory systems
function and affect correctness, performance, and optimization.
|Objective||The course objectives are for students to:|
1. Develop a deep understanding of, and intuition about, the execution
of all the layers (compiler, runtime, OS, etc.) between programs in
high-level languages and the underlying hardware: the impact of
compiler decisions, the role of the operating system, the effects
of hardware on code performance and scalability, etc.
2. Be able to write correct, efficient programs on modern hardware,
not only in C but high-level languages as well.
3. Understand Systems Programming as a complement to other disciplines
within Computer Science and other forms of software development.
This course does not cover how to design or build a processor or
|Content||This course provides an overview of "computers" as a|
platform for the execution of (compiled) computer programs. This
course provides a programmer's view of how computer systems execute
programs, store information, and communicate. The course introduces
the major computer architecture structures that have direct influence
on the execution of programs (processors with registers, caches, other
levels of the memory hierarchy, supervisor/kernel mode, and I/O
structures) and covers implementation and representation issues only
to the extend that they are necessary to understand the structure and
operation of a computer system.
The course attempts to expose students to the practical issues that
affect performance, portability, security, robustness, and
extensibility. This course provides a foundation for subsequent
courses on operating systems, networks, compilers and many other
courses that require an understanding of the system-level
issues. Topics covered include: machine-level code and its generation
by optimizing compilers, address translation, input and output,
trap/event handlers, performance evaluation and optimization (with a
focus on the practical aspects of data collection and analysis).
|Lecture notes||- C programmnig|
- Pointers and dynamic memory allocation
- Basic computer architecture
- Compiling C control flow and data structures
- Code vulnerabilities
- Implementing memory allocation
- Floating point
- Optimizing compilers
- Architecture and optimization
- Virtual memory
|Literature||The course is based in part on "Computer Systems: A Programmer's Perspective" (3rd Edition) by R. Bryant and D. O'Hallaron, with additional material.|
|Prerequisites / Notice||252-0029-00L Parallel Programming|
252-0028-00L Design of Digital Circuits
|401-0213-16L||Analysis II||O||5 credits||2V + 2U||Ö. Imamoglu|
|Abstract||Differential and Integral calculus in many variables, vector analysis.|
|Objective||Differential and Integral calculus in many variables, vector analysis.|
|Content||Differential and Integral calculus in many variables, vector analysis.|
|Literature||Für allgemeine Informationen, sehen Sie bitte die Webseite der Vorlesung: https://metaphor.ethz.ch/x/2017/hs/401-0213-16L/|
|401-0663-00L||Numerical Methods for CSE||O||7 credits||4V + 2U||R. Alaifari|
|Abstract||The course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++.|
|Objective||* Knowledge of the fundamental algorithms in numerical mathematics|
* Knowledge of the essential terms in numerical mathematics and the
techniques used for the analysis of numerical algorithms
* Ability to choose the appropriate numerical method for concrete problems
* Ability to interpret numerical results
* Ability to implement numerical algorithms afficiently
|Content||1. Direct Methods for linear systems of equations|
2. Least Squares Techniques
3. Data Interpolation and Fitting
4. Filtering Algorithms
8. Approximation of Functions
9. Numerical Quadrature
10. Iterative Methods for non-linear systems of equations
11. Single Step Methods for ODEs
12. Stiff Integrators
|Lecture notes||Lecture materials (PDF documents and codes) will be made available to the participants through the course web page:|
|Literature||U. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011.|
A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000.
W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006.
M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002
P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002
|Prerequisites / Notice||The course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Familiarity with C++, object oriented and generic programming is an advantage. Participants of the course are expected to learn C++ by themselves.|
|Major: Information and Data Processing|
|252-0206-00L||Visual Computing||O||8 credits||4V + 3U||S. Coros, O. Hilliges|
|Abstract||This course acquaints students with core knowledge in computer graphics, image processing, multimedia and computer vision. Topics include: Graphics pipeline, perception and camera models, transformation, shading, global illumination, texturing, sampling, filtering, image representations, image and video compression, edge detection and optical flow.|
|Objective||This course provides an in-depth introduction to the core concepts of computer graphics, image processing, multimedia and computer vision. The course forms a basis for the specialization track Visual Computing of the CS master program at ETH.|
|Content||Course topics will include: Graphics pipeline, perception and color models, camera models, transformations and projection, projections, lighting, shading, global illumination, texturing, sampling theorem, Fourier transforms, image representations, convolution, linear filtering, diffusion, nonlinear filtering, edge detection, optical flow, image and video compression.|
In theoretical and practical homework assignments students will learn to apply and implement the presented concepts and algorithms.
|Lecture notes||A scriptum will be handed out for a part of the course. Copies of the slides will be available for download. We will also provide a detailed list of references and textbooks.|
|Literature||Markus Gross: Computer Graphics, scriptum, 1994-2005|
|Major: Theoretical Computer Science|
|252-0209-00L||Algorithms, Probability, and Computing||O||8 credits||4V + 2U + 1A||E. Welzl, M. Ghaffari, A. Steger, D. Steurer, P. Widmayer|
|Abstract||Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).|
|Objective||Studying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory.|
|Lecture notes||Will be handed out.|
|Literature||Introduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest;|
Randomized Algorithms by R. Motwani und P. Raghavan;
Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf.
|252-3110-00L||Human Computer Interaction||W||4 credits||2V + 1U||O. Hilliges, M. Norrie|
|Abstract||The course provides an introduction to the field of human-computer interaction, emphasising the central role of the user in system design. Through detailed case studies, students will be introduced to different methods used to analyse the user experience and shown how these can inform the design of new interfaces, systems and technologies.|
|Objective||The goal of the course is that students should understand the principles of user-centred design and be able to apply these in practice.|
|Content||The course will introduce students to various methods of analysing the user experience, showing how these can be used at different stages of system development from requirements analysis through to usability testing. Students will get experience of designing and carrying out user studies as well as analysing results. The course will also cover the basic principles of interaction design. Practical exercises related to touch and gesture-based interaction will be used to reinforce the concepts introduced in the lecture. To get students to further think beyond traditional system design, we will discuss issues related to ambient information and awareness.|
|151-0107-20L||High Performance Computing for Science and Engineering (HPCSE) I||W||4 credits||4G||P. Koumoutsakos, P. Chatzidoukas|
|Abstract||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.|
|Objective||Introduction to HPC for scientists and engineers|
1. Parallel Computing Architectures
|Content||Programming models and languages:|
1. C++ threading (2 weeks)
2. OpenMP (4 weeks)
3. MPI (5 weeks)
Computers and methods:
1. Hardware and architectures
3. Particles: N-body solvers
4. Fields: PDEs
5. Stochastics: Monte Carlo
Class notes, handouts
|227-0627-00L||Applied Computer Architecture||W||6 credits||4G||A. Gunzinger|
|Abstract||This lecture gives an overview of the requirements and the architecture of parallel computer systems, performance, reliability and costs.|
|Objective||Understand the function, the design and the performance modeling of parallel computer systems.|
|Content||The lecture "Applied Computer Architecture" gives technical and corporate insights in the innovative Computer Systems/Architectures (CPU, GPU, FPGA, special processors) and their real implementations and applications. Often the designs have to deal with technical limits.|
Which computer architecture allows the control of the over 1000 magnets at the Swiss Light Source (SLS)?
Which architecture is behind the alarm center of the Swiss Railway (SBB)?
Which computer architectures are applied for driver assistance systems?
Which computer architecture is hidden behind a professional digital audio mixing desk?
How can data streams of about 30 TB/s, produced by a protone accelerator, be processed in real time?
Can the weather forecast also be processed with GPUs?
How can a good computer architecture be found?
Which are the driving factors in succesful computer architecture design?
|Lecture notes||Script and exercices sheets.|
|Prerequisites / Notice||Prerequisites: |
Basics of computer architecture.
|227-0945-00L||Cell and Molecular Biology for Engineers I|
This course is part I of a two-semester course.
|W||3 credits||3G||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.|
|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: 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, three journal clubs will be held, where one/two publictions will be discussed (part I: 1 Journal club, part II: 2 Journal Clubs). 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 25% 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.|
Entry level course in management for BSc, MSc and PHD students at all levels not belonging to D-MTEC. This course can be complemented with Discovering Management (Excercises) 351-0778-01.
|W||3 credits||3G||B. Clarysse, M. Ambühl, S. Brusoni, E. Fleisch, G. Grote, V. Hoffmann, T. Netland, G. von Krogh, F. von Wangenheim|
|Abstract||Discovering Management offers an introduction to the field of business management and entrepreneurship for engineers and natural scientists. The module provides an overview of the principles of management, teaches knowledge about management that is highly complementary to the students' technical knowledge, and provides a basis for advancing the knowledge of the various subjects offered at D-MTEC.|
|Objective||Discovering Management combines in an innovate format a set of lectures and an advanced business game. The learning model for Discovering Management involves 'learning by doing'. The objective is to introduce the students to the relevant topics of the management literature and give them a good introduction in entrepreneurship topics too. The course is a series of lectures on the topics of strategy, innovation, corporate finance, leadership, design thinking and corporate social responsibility. While the 14 different lectures provide the theoretical and conceptual foundations, the experiential learning outcomes result from the interactive business game. The purpose of the business game is to analyse the innovative needs of a large multinational company and develop a business case for the company to grow. This business case is as relevant to someone exploring innovation within an organisation as it is if you are planning to start your own business. By discovering the key aspects of entrepreneurial management, the purpose of the course is to advance students' understanding of factors driving innovation, entrepreneurship, and company success.|
|Content||Discovering Management aims to broaden the students' understanding of the principles of business management, emphasizing the interdependence of various topics in the development and management of a firm. The lectures introduce students not only to topics relevant for managing large corporations, but also touch upon the different aspects of starting up your own venture. The lectures will be presented by the respective area specialists at D-MTEC.|
The course broadens the view and understanding of technology by linking it with its commercial applications and with society. The lectures are designed to introduce students to topics related to strategy, corporate innovation, leadership, corporate and entrepreneurial finance, value chain analysis, corporate social responsibility, and business model innovation. Practical examples from industry experts will stimulate the students to critically assess these issues. Creative skills will be trained by the business game exercise, a participant-centered learning activity, which provides students with the opportunity to place themselves in the role of Chief Innovation Officer of a large multinational company. As they learn more about the specific case and identify the challenge they are faced with, the students will have to develop an innovative business case for this multinational corporation. Doing so, this exercise will provide an insight into the context of managerial problem-solving and corporate innovation, and enhance the students' appreciation for the complex tasks companies and managers deal with. The business game presents a realistic model of a company and provides a valuable learning platform to integrate the increasingly important development of the skills and competences required to identify entrepreneurial opportunities, analyse the future business environment and successfully respond to it by taking systematic decisions, e.g. critical assessment of technological possibilities.
|Prerequisites / Notice||Discovering Management is designed to suit the needs and expectations of Bachelor students at all levels as well as Master and PhD students not belonging to D-MTEC. By providing an overview of Business Management, this course is an ideal enrichment of the standard curriculum at ETH Zurich.|
No prior knowledge of business or economics is required to successfully complete this course.
Not for MSc students belonging to D-MTEC!
|W||4 credits||3V||S. Rausch, V. Hoffmann|
|Abstract||Managerial Economics applies economic theory and methods to business and economic decision-making. Economic ideas related to optimization, the theory of consumer demand, the theory of the firm, industrial organization and decision making under uncertainty are studied using methods of numerical analysis, statistical estimation, game theory and constrained optimization.|
|Objective||The objective of the course is to provide undergraduate and graduate students in MAVT with an understanding of the use of economic concepts for firm-level management decisions. The course covers a number of models and methods of analysis which are commonly employed in business decisions. The course covers the economic theory of choice, models of oligopoly and industrial organization, applications of game theory to contract design and agency theory, and the theory of decision making under uncertainty focusing specifically on long-term investment decisions. The course will include three lectures by Professor Volker Hoffman focusing on related case-studies in management.|
|Literature||Mikroökonomie (Pearson Studium - Economic VWL) Gebundene Ausgabe, August 2013, Robert S. Pindyck, Dr. Daniel L. Rubinfeld.|
|Prerequisites / Notice||The course acquaints students who have previous not studied economics to economic concepts and quantitative methods which can be used to solve management decision problems.|
|363-0585-00L||Intermediate Econometrics||W||3 credits||2V||M. Kesina|
|Abstract||The idea of this course is to familiarize students with instrumental variables estimation of linear regression models and the estimation of models with limited dependent variables as well as of nonlinear regression models. While most of the material covered will pertain to cross-sectional data, we will also work on selected issues with panel data.|
|Objective||I will provide STATA programs and show the execution thereof. After having participated in this course, students will be able to carry out simple research projects and understand the basics of intermediate econometrics. In particular, they will be able to write simple programs in STATA and to qualify their own and others' regression output relating to problems covered.|
|Literature||Jeffrey M. Wooldridge: Introductory Econometrics; Jeffrey M. Wooldridge: Econometric Analysis of Cross Section and Panel Data; A. Colin Cameron and Pravin K. Trivedi. Microeconometrics: Methods and Applications.|
|363-1047-00L||Economics of Urban Transportation||W||3 credits||2G||A. Russo|
|Abstract||The first part of the course will present some basic principles of transportation economics, applied to the main issues in urban transport policy (e.g. road pricing, public transport tariffs, investment in infrastructure etc.). The second part of the course will consider some case studies where we will apply the tools acquired in the first part to actual policy issues.|
|Objective||The main objective of this course is to provide students with some basic tools to analyze transport policy decisions from an economic perspective. Can economics help us reduce road congestion problems? Should drivers be asked to pay for using urban roads? Should public transport tariffs depend on how roads are priced? How should the investment in transport infrastructure be financed? These are some of the questions that students should be able to tackle after completing the course.|
|Content||COURSE OUTLINE (preliminary):|
2. Travel demand :
a. travel cost and value of time
b. mode choice
3. Road congestion and first-best pricing
a. Static congestion model
b. Dynamic congestion models
c. Examples: London Congestion Charge, Stockholm Congestion Charge
4. Second-best pricing
a. Pricing roads with unpriced alternatives. Examples: tolled and toll-free highways
b. Public transport: pricing with road congestion and with (or without) road tolls
5. Investment in infrastructure: public transport and roads
a. Roads: Investment with and without pricing
b. induced demand
c. Economies of scale/density in public transport
a. Political economy of road pricing: why do we see road pricing in so few cities (London, Stockholm...) and not in many other cities (NYC, Manchester, Paris...)?
b. What are the alternatives to road pricing to reduce congestion? Parking tariffs, traffic regulation (speed bumps, low emission zones), road space reduction. Examples: Zurich, San Francisco (SFPark), Paris.
c. Transport and land use: value of housing and transport services. Road congestion, transport subsidies and urban sprawl.
|Lecture notes||Course slides will be made available to students prior to each class.|
|Literature||SYLLABUS (preliminary): |
course slides will be made available to students.
Part 1 to 5: textbook: Small and Verhoef (The economics of urban transportation, 2007).
Part 6: Topics to be covered on research papers/case studies.
|376-1177-00L||Human Factors I||W||3 credits||2V||M. Menozzi Jäckli, R. Huang, M. Siegrist|
|Abstract||Every day humans interact with various systems. Strategies of interaction, individual needs, physical & mental abilities, and system properties are important factors in controlling the quality and performance in interaction processes. In the lecture, factors are investigated by basic scientific approaches. Discussed topics are important for optimizing people's satisfaction & overall performance.|
|Objective||The goal of the lecture is to empower students in better understanding the applied theories, principles, and methods in various applications. Students are expected to learn about how to enable an efficient and qualitatively high standing interaction between human and the environment, considering costs, benefits, health, and safety as well. Thus, an ergonomic design and evaluation process of products, tasks, and environments may be promoted in different disciplines. The goal is achieved in addressing a broad variety of topics and embedding the discussion in macroscopic factors such as the behavior of consumers and objectives of economy.|
|Content||- Physiological, physical, and cognitive factors in sensation and perception|
- Body spaces and functional anthropometry, Digital Human Models
- Experimental techniques in assessing human performance and well-being
- Human factors and ergonomics in system designs, product development and innovation
- Human information processing and biological cybernetics
- Interaction among consumers, environments, behavior, and tasks
|Literature||- Gavriel Salvendy, Handbook of Human Factors and Ergonomics, 4th edition (2012), is available on NEBIS as electronic version and for free to ETH students|
- Further textbooks are introduced in the lecture
- Brouchures, checklists, key articles etc. are uploaded in ILIAS
|401-7855-00L||Computational Astrophysics (University of Zurich)|
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: AST245
Mind the enrolment deadlines at UZH:
|W||6 credits||2V||L. M. Mayer|
|Objective||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|
|Content||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
|Literature||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)
|Prerequisites / Notice||Some knowledge of UNIX, scripting languages (see www.physik.uzh.ch/lectures/informatik/python/ as an example), some prior experience programming, knowledge of C, C++ beneficial|
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