Search result: Catalogue data in Spring Semester 2021
Spatial Development and Infrastructure Systems Master  | ||||||
 2. Semester | ||||||
| Major Courses | ||||||
| Major Courses for all majors | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|---|
| 103-0427-00L | Regional Economics | W | 4 credits | 2G | B. Buser, C. Abegg | |
| Abstract | The lecture on Regional Economics focusses on the theoretical aspects of spatial factor allocation and of growth determinants. The course takes a top down stance and looks at regional development from a macroeconomic perspective. Implications of theoretical models on regional and growth policy will be discussed in and connections to the course Site Management will be made. | |||||
| Learning objective | Students shall know the theoretical basics of spatial economy and growth theories an a regional scale; they shall gain the competence to apply concepts and theories of spatial science as well as regional economics to concrete problems of their area of study. | |||||
| Content | Origin of "Spatial Economics" Indices of regional economics and growth analysis Regional advantages in competition and growth theories Regional innovation theory (innovation processes, cluster theory and innovation policy) Theory and political implications with examples (New Regional Policy NRP, Regional Innovation Systems RIS) External Speaker and discussion of topicality by press | |||||
| Lecture notes | Download two days before lecture: http://irl.ethz.ch/de/education/vorlesungen/msc/regional_economics.html https://ilias-app2.let.ethz.ch/goto.php?target=crs_118394&client_id=ilias_lda ETH members can view the recordings of the lecture at: https://video.ethz.ch/lectures/d-baug/2021/spring/103-0427-00L.html | |||||
| Literature | Literature is optional, there will be given hints to: Bathelt, H., Glückler J. (2012): Wirtschaftsgeographie. Ökonomische Beziehungen in räumlicher Perspektive. 3. Auflage. ISBN: 978-3-8252-8492-3 Eisenhut, P. (2014): Aktuelle Volkswirtschaftslehre 2018/2019. Rüegger Verlag, Zürich. ISBN: 978-3-7253-1066-1 Eckey, H.-F. (2008): Regionalökonomie. GWV Fachverlag GmbH, Wiesbaden. ISBN: 978-3-8349-0999-2 | |||||
| Prerequisites / Notice | The lecture will be held online on Zoom. Access data can be requested from Mr. Sergio Wicki (sergio.wicki@sec.ethz.ch). | |||||
| 363-1039-00L | Introduction to Negotiation | W | 3 credits | 2G | M. Ambühl | |
| Abstract | The course introduces students to the concepts, theories, and strategies of negotiation and is enriched with an extensive exploration of real-life case-study examples. | |||||
| Learning objective | The objective of the course is to teach students to recognize, understand, and approach different negotiation situations, by relying on a range of primarily quantitative and some qualitative analytical tools. | |||||
| Content | We all negotiate on a daily basis – on a personal level with friends, family, and service providers, on a professional level with employers and clients, among others. Additionally, negotiations are constantly unfolding across various issues at the political level, from solving armed conflicts to negotiating trade and market access deals. The course aims to provide students with a toolbox of analytical methods that can be used to identify and disentangle negotiation situations, as well as serve as a reference point to guide action in practice. The applicability of these analytical methods is illustrated through examples of negotiation situations from international politics and business. The theoretical part of the course covers diverse perspectives on negotiation: with a key focus on game theory, but also covering Harvard principles of negotiation, as well as the negotiation engineering approach developed by Prof. Ambühl at ETH Zurich. The course also dedicates some time to focus on conflict management as a specific category of negotiation situations and briefly introduces students to the social aspects of negotiation, based on the insights from psychology and behavioral economics. The empirical part of the course draws on case-studies from the realm of international politics and business, including examples from Prof. Ambühl’s work as a career diplomat. Every year, the course also hosts two guest lecturers – representatives from politics or business leaders, who share practical experience on negotiations from their careers. | |||||
| Literature | The list of relevant references will be distributed in the beginning of the course. | |||||
| 701-1674-00L | Spatial Analysis, Modelling and Optimisation  Number of participants limited to 25. Prerequisites: 701-0951-00L "GIS - Introduction into Geoinformation Science" in autum semester or comparable preparatory training. | W | 5 credits | 4G | M. A. M. Niederhuber, V. Griess | |
| Abstract | Problems encountered in forest- and landscape management often have a spatial dimension. Methods and technics of geoinformation sciences GIS and/or optimization give support to identify good solutions. Students learn to conceptualize, implement and combine I) spatial analysis & modeling of geodata and, II) optimization techniques, based on theoretical inputs and practical work on small projects. | |||||
| Learning objective | Understand, search for, and manage various types of geospatial data; Carry out conceptual data modelling for a spatial and/or optimisation problem and translate it into a tangible form within a GIS software; Conceptualize spatial and/or optimisation problems and design a workflow that transitions from "data processing" through "advanced spatial analysis" to "presentation of results"; Implement such a workflow in standard GIS and/or optimisation software, verify and validate the procedures, then present the final results. | |||||
| Prerequisites / Notice | Knowledge and skills equal those of the course "GIST - Einführung in die räumliche Informationswissenschaften und Technologien" | |||||
| 103-0488-00L | Seminar in Spatial Development and Infrastructure Systems | W | 9 credits | 18A | Supervisors | |
| Abstract | This seminar offers the students the opportunity to research and present a topic of their choice in depth resulting in a term paper. The topic can be freely chosen after consultation with the chair supervising the student. The chairs will also provide a list of proposed topics. | |||||
| Learning objective | Practise independent scientific working addressing a relevant topic from the range of the master's programme course. | |||||
| Content | The students can work on a topic of their choice from the range of the he master's programme course. | |||||
| 103-0517-00L | Urban and Spatial Economics | W | 3 credits | 2V | R. H. van Nieuwkoop | |
| Abstract | This course explores the economic factors which influence location decisions of households and firms, and it explores theories of how these decisions induce the formation of cities. The course will cover the neoclassical models of land use, concepts from the new economic geography, zoning, and transportation and traffic congestion. | |||||
| Learning objective | The objective of the course is to provide graduate students with an understanding of the economic factors which give rise to urban spatial structure and the models which have been employed to study these processes. The course aims to help students develop an appreciation for the use of economic models in both positive and normative frameworks. We will assess both the history of thought regarding the role of markets in creating urban development, and we will read about modern theories of externalities and economic factors which induce agglomeration. The final section of the course will focus on transportation problems in urban areas and the use of economic models to assess public policy measures to deal with congestion and associated externalities. | |||||
| Content | Outline of Lectures Topic 1: Why do cities exist? Topic 2: The Basic Muth-Mills model Topic 3: The New Economic Geography Topic 4: Business demand for land and Von Thünen's model) Topic 5: Urban spatial structure Topic 6: Land use control Topic 7: City size and city growth Topic 8: Traffic externalities and congestion Topic 9: Public transport Topic 10: The housing crisis | |||||
| Literature | Textbook o Urban Economics by Arthur O'Sullivan, McGraw-Hill. Ancillary Texts o Lectures on Urban Economics, K. Brückner, 2011, The MIT Press o Cities, agglomeration and spatial equilibrium by E. L. Glaeser, 2008, Oxford University Press. o A Companion to Urban Economics, Richard Arnott and Daniel McMillen (eds.), Blackwell, 2006. o The new introduction to geographical economics, Steven Brakman, Harry Garretsen and Charles van Marrewijk, Cambridge. o Urban transport economics, by K. A. Small and E. Verhoef, Routledge. | |||||
| 101-0521-10L | Machine Learning for Predictive Maintenance Applications The number of participants in the course is limited to 25 students. Students interested in attending the lecture are requested to upload their transcript and a short motivation responding the following two questions (max. 200 words): -How does this course fit to the other courses you have attended so far? -How does the course support you in achieving your goal? The following link can be used to upload the documents. https://polybox.ethz.ch/index.php/s/3S9ZlyxQTiOS3fM | W | 8 credits | 4G | O. Fink | |
| Abstract | The course aims at developing machine learning algorithms that are able to use condition monitoring data efficiently and detect occurring faults in complex industrial assets, isolate their root cause and ultimately predict the remaining useful lifetime. | |||||
| Learning objective | Students will - be able to understand the main challenges faced by predictive maintenance systems - learn to extract relevant features from condition monitoring data -learn to select appropriate machine learning algorithms for fault detection, diagnostics and prognostics -learn to define the learning problem in way that allows its solution based on existing constrains such as lack of fault samples. - learn to design end-to-end machine learning algorithms for fault detection and diagnostics -be able to evaluate the performance of the applied algorithms. At the end of the course, the students will be able to design data-driven predictive maintenance applications for complex engineered systems from raw condition monitoring data. | |||||
| Content | Early and reliable detection, isolation and prediction of faulty system conditions enables the operators to take recovery actions to prevent critical system failures and ensure a high level of availability and safety. This is particularly crucial for complex systems such as infrastructures, power plants and aircraft engines. Therefore, their system condition is increasingly tightly monitored by a large number of diverse condition monitoring sensors. With the increased availability of data on system condition on the one hand, and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for predictive maintenance has been recently increasing. This course provides insights and hands-on experience in selecting, designing, optimizing and evaluating machine learning algorithms to tackle the challenges faced by maintenance systems of complex engineered systems. Specific topics include: -Introduction to condition monitoring and predictive maintenance systems -Feature extraction and selection methodology -Machine learning algorithms for fault detection and fault isolation -End-to-end learning architectures (including feature learning) for fault detection and fault isolation -Unsupervised and semi-supervised learning algorithms for predictive maintenance -Machine learning algorithms for prediction of the remaining useful life -Performance evaluation -Predictive maintenance systems at fleet level -Domain adaptation for fault diagnostics -Introduction to decision support systems for maintenance applications | |||||
| Lecture notes | Slides and other materials will be available online. | |||||
| Literature | Relevant scientific papers will be discussed in the course. | |||||
| Prerequisites / Notice | Strong analytical skills. Programming skills in python are strongly recommended. | |||||
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