Suchergebnis: Katalogdaten im Frühjahrssemester 2024
Science, Technology, and Policy Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Sozialwissenschaftliche Fächer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 860-0005-01L | Colloquium Science, Technology, and Policy (FS)  | O | 1 KP | 1K | T. Schmidt, T. Bernauer, E. Tilley | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The colloquium consists of presentations by guest speakers from academia and practice/policy. Students are assigned to play a leading role in the discussion and write a report on the respective event. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students meet guest speakers from academia and practice/policy. Students are assigned to play a leading role in the discussion and write a report on the respective event. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | See program on the ISTP website: http://www.istp.ethz.ch/events/colloquium.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 860-0001-00L | Public Institutions and Policy-Making Processes Number of participants limited to 27. Priority for Science, Technology, and Policy Master. | O | 3 KP | 2G | T. Bernauer, S. Bechtold, F. Schimmelfennig | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Students acquire the contextual knowledge for analyzing public policies. They learn why and how public policies and laws are developed, designed, and implemented at national and international levels, and what challenges arise in this regard. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Public policies result from decision-making processes that take place within formal institutions of the state (parliament, government, public administration, courts). That is, policies are shaped by the characteristics of decision-making processes and the characteristics of public institutions and related actors (e.g. interest groups). In this course, students acquire the contextual knowledge for analyzing public policies. They learn why and how public policies and laws are developed, designed, and implemented at national and international levels, and what challenges arise in this regard. The course is organized in three modules. The first module (Stefan Bechtold) examines basic concepts and the role of law, law-making, and law enforcement in modern societies. The second module (Thomas Bernauer) deals with the functioning of legislatures, governments, and interest groups. The third module (Frank Schimmelfennig) focuses on the European Union and international organisations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Public policies result from decision-making processes that take place within formal institutions of the state (parliament, government, public administration, courts). That is, policies are shaped by the characteristics of decision-making processes and the characteristics of public institutions and related actors (e.g. interest groups). In this course, students acquire the contextual knowledge for analyzing public policies. They learn why and how public policies and laws are developed, designed, and implemented at national and international levels, and what challenges arise in this regard. The course is organized in three modules. The first module (Stefan Bechtold) examines basic concepts and the role of law, law-making, and law enforcement in modern societies. The second module (Thomas Bernauer) deals with the functioning of legislatures, governments, and interest groups. The third module (Frank Schimmelfennig) focuses on the European Union and international organisations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Course materials can be found on Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Readings can be found on Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This is a Master level course. The course is capped at 27 students, with ISTP Master students having priority. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 860-0042-00L | Statistics 2 | O | 4 KP | 1G | K. Harttgen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course introduces students to key statistical methods for analyzing social science data with a special emphasis on causal inference and policy evaluation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students - have a sound understanding of standard regression techniques - know strategies to test causal hypotheses using regression analysis and/or experimental methods - are able to formulate and implement a regression model for a particular policy question and a particular type of data - are able to critically interpret results of applied statistics, in particular, regarding causal inference - are able to critically read and assess published studies on policy evaluation - are able to use the statistical software Stata for data analysis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The topics covered in the first part of the course are a revision and linear regression and non-linear regression techniques such as probit and logit regression analysis. The second part of the course focuses on causal inference and introduces methods such as panel data analysis, difference-in-difference methods, instrumental variable estimation, regression discontinuity design, and randomized controlled trials used for policy evaluation. The course shows how the various methods differ in terms of the required identifying assumptions to infer causality as well as the data needs. Students will apply the methods from the lectures by solving bi-weekly assignments using statistical software and data sets provided by the instructors. These data sets will cover topics at the interface of policy, technology and society. Solving the assignments contributes to the final grade with a weight of 30%. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Keines. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 860-0033-00L | Data Science for Public Policy: From Econometrics to AI Only for Master students and PhD students. | O | 3 KP | 2G | S. Galletta, E. Ash, C. Gössmann | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides an introduction to big data methods for public policy analysis. Students will put these techniques to work on a course project using real-world data, to be designed and implemented in consultation with the instructors. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Many policy problems involve prediction. For example, a budget office might want to predict the number of applications for benefits payments next month, based on labor market conditions this month. This course provides a hands-on introduction to the "big data" techniques for making such predictions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Many policy problems involve prediction. For example, a budget office might want to predict the number of applications for benefits payments next month, based on labor market conditions this month. This course provides a hands-on introduction to the "big data" techniques for making such predictions. These techniques include: -- procuring big datasets, especially through web scraping or API interfaces, including social media data; -- pre-processing and dimension reduction of massive datasets for tractable computation; -- machine learning for predicting outcomes, including how to select and tune the model, evaluate model performance using held-out test data, and report results; -- interpreting machine learning model predictions to understand what is going on inside the black box; -- data visualization including interactive web apps. Students will put these techniques to work on a course project using real-world data, to be designed and implemented in consultation with the instructors. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | https://github.com/gochristoph/data-science-for-public-policy-2024 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| Naturwissenschaftlich-technische Ergänzung | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Städte, Infrastruktur und Planung | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 102-0838-00L | Water Supply, Sanitation and Waste Infrastructure and Services in Developing Countries | W | 3 KP | 2G | L. Strande | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction to water supply, excreta, wastewater and solid waste management in developing countries. Highlights links between infrastructure, services and health, resource conservation and environmental protection. New concepts and approaches for sustainable sanitation infrastructure and services for developing countries - especially poor urban areas. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students receive an introduction to issues of water supply, excreta, waste water and solid waste management in developing countries. They understand the connections between water, wastewater and waste management, health, resource conservation and environmental protection. Besides, they learn how water supply, wastewater and solid waste infrastructure and services can be combined and improved, in order to achieve the development policy goals in terms of disease prevention, resource conservation, and environmental protection. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Overview of the global health situation, water supply, and liquid and solid waste management situation in developing countries. Technical and scientific fundamentals of water supply, sanitation and solid waste management. Material flows in water supply, sanitation and waste management. New concepts and approaches for sustainable sanitation infrastructure and services for developing countries - especially poor urban areas. Exercises: students will work in groups on a case study and develop improvement options for water, sanitation and waste management. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Course notes and further reading will be made available on the ETHZ Moodle portal. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | The selected literature references will be made available on Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Students will work in groups on a case study and develop improvement options for water, sanitation and waste management. The case study work will be marked (1/3 of final grade). Written Semesterendprüfung of 90 min (counts for 2/3 of final grade) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0481-00L | Readings in Transport Policy | W | 3 KP | 2G | L. Meyer de Freitas | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course will explore the issues and constraints of transport policy through the joint readings of a set of relevant papers. The class will meet every three weeks to discuss the texts. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Familiarize the students with issues of transport policy making and the conflicts arising. Train the ability to read critically and to summarize his/her understanding for him/herself and others through a review paper, paper abstracts and a paper review. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 102-0338-01L | Waste Management and Circular Economy | W | 3 KP | 2G | M. Haupt, R. Warthmann, M. Wiprächtiger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Understanding the fundamental concepts of advanced waste management and circular economy and, in more detail, on biological processes for waste treatment. Application of concepts on various waste streams, including household and industrial waste streams. Insights into environmental aspects of different waste treatment technologies and waste economy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The purpose of this course is to study the fundamental concepts of waste management in Switzerland and globally and learn about new concepts such as Circular Economy. In-depth knowledge on biological processes for waste treatments should be acquired and applied in case studies. Based on this course, you should be able to understand national waste management strategies and related treatment technologies. Treatment plants and valorization concepts for biomass and organic waste should be understood. Furthermore, future designs of waste treatment processes can be evaluated using basic process understanding and knowledge obtained from the current literature. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | National waste management Waste as a resource Circular Economy Assessment tools for waste management strategies Plastic recycling Organic Wastes in Switzerland Anaerobic Digestion & Biogas Composting process technologies Organic Waste Hygiene Product Quality & Use Waste Economy and environmental aspects | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handouts Exercises based on literature | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Deublein, D. and Steinhauser, A. (2011): Biogas from Waste and Renewable Resources: An Introduction. 2nd Edition, Wiley VCH, Weinheim. --> One of the leading books on the subject of anaerobic digestion and biogas, covering all aspects from biochemical and microbial basics to planning and running of biogas plants as well as different technology concepts and biogas upgrade & utilization. Haupt, M., C. Vadenbo, and S. Hellweg. 2017. Do We Have the Right Performance Indicators for the Circular Economy?: Insight into the Swiss Waste Management System. Journal of Industrial Ecology 21(3): 615–627. Haupt, M. and S. Hellweg. 2019. Measuring the environmental sustainability of a circular economy. Environmental and Sustainability Indicators Volumes 1–2, September 2019, 100005. More information about biowaste treatment in Switzerland (www.cvis.ch) and Europe (www.compostnetwork.info and www.ecn-qas.eu) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | There will be complementary exercises going along with some of the lectures, which focus on real life aspects of waste management. Some of the exercises will be solved during lessons whereas others will have to be dealt with as homework. To pass the course and to achieve credits it is required to pass the examination successfully (Mark 4 or higher). The written examination covers all topics of the course and is based on handouts and on selected literature | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0588-01L | Re-/Source the Built Environment | W | 3 KP | 2S | G. Habert, M. Posani, E. Zea Escamilla | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course focuses on material choice and energy strategies to limit the environmental impact of construction sector. During the course, specific topics will be presented (construction technologies, environmental policies, social consequences of material use, etc.). The course aims to present sustainable options to tackle the global challenge we are facing and show that "it is not too late". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After the lecture series, the students are aware of the main challenges for the production and use of building materials. They know the different technologies/propositions available, and environmental consequence of a choice. They understand in which conditions/context one resource/technology will be more appropriate than another | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | A general presentation of the global context allows to identify the objectives that as engineer, material scientist or architect needs to achieve to create a sustainable built environment. The course is then conducted as a serie of guest lectures focusing on one specific aspect to tackle this global challenge and show that "it is not too late". The lecture series is divided as follows: - General presentation - Notion of resource depletion, resilience, criticality, decoupling, etc. - Guest lectures covering different resources and proposing different option to build or maintain a sustainable built environment. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | For each lecture slides will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The lecture series will be conducted in English and is aimed at students of master's programs, particularly the departments ARCH, BAUG, ITET, MAVT, MTEC and USYS. No lecture will be given during Seminar week. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0478-00L | Survey Methods and Discrete Choice Analysis | W | 6 KP | 4G | B. Schmid | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Comprehensive introduction to survey methods in transport planning and modeling of travel behavior, using advanced discrete choice models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Enabling the student to understand and apply the various measurement approaches and models of travel behaviour research. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Behavioral model and measurement; travel diary, design process, hypothetical markets, parameter estimation, econometrics, pattern of travel behaviour, market segments, simulation, advanced discrete choice models | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Various papers and notes are distributed during the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | The course heavily builds on Train, K. E. (2009) Discrete Choice Methods with Simulation, Cambridge University Press. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This introduction in survey methods and (advanced) discrete choice modelling requires basic programming knowledge in the statistical software R. Solid understanding of statistical modeling and econometrics is of advantage. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| Mobilität und Energie | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 529-0191-01L | Electrochemical Energy Conversion and Storage Technologies | W | 4 KP | 2V + 1U | L. Gubler, J. Herranz Salañer, S. Trabesinger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course provides an introduction to the principles and applications of electrochemical energy conversion (e.g. fuel cells) and storage (e.g. batteries) technologies in the broader context of a renewable energy system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students will discover the importance of electrochemical energy conversion and storage in energy systems of today and the future, specifically in the framework of renewable energy scenarios. Basics and key features of electrochemical devices will be discussed, and applications in the context of the overall energy system will be highlighted with focus on future mobility technologies and grid-scale energy storage. Finally, the role of (electro)chemical processes in power-to-X and deep decarbonization concepts will be elaborated. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Overview of energy utilization: past, present and future, globally and locally; today’s and future challenges for the energy system; climate changes; renewable energy scenarios; introduction to electrochemistry; electrochemical devices, basics and their applications: batteries, fuel cells, electrolyzers, flow batteries, supercapacitors, chemical energy carriers: hydrogen & synthetic natural gas; electromobility; grid-scale energy storage, power-to-gas, power-to-X and deep decarbonization, techno-economics and life cycle analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | all lecture materials will be available for download on the course website and Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Textbook recommendations for advanced studies on the topics of the course: - M. Sterner, I. Stadler (Eds.): Handbook of Energy Storage (Springer, 2019). - C.H. Hamann, A. Hamnett, W. Vielstich; Electrochemistry, Wiley-VCH (2007). - T.F. Fuller, J.N. Harb: Electrochemical Engineering, Wiley (2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Basic physical chemistry background required, prior knowledge of electrochemistry basics desired. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 151-0928-00L | CO2 Capture and Storage and the Industry of Carbon-Based Resources | W | 4 KP | 3G | A. Bardow, V. Becattini, N. Gruber, M. Mazzotti, M. Repmann, T. Schmidt, D. Sutter | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course introduces the fundamentals of carbon capture, utilization, and storage and related interdependencies between technosphere, ecosphere, and sociosphere. Topics covered: origin, production, processing, and economics of carbon-based resources; climate change in science & policies; CC(U)S systems; CO2 transport & storage; life-cycle assessment; net-zero emissions; CO2 removal options. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The lecture aims to introduce carbon dioxide capture, utilization, and storage (CCUS) systems, the technical solutions developed so far, and current research questions. This is done in the context of the origin, production, processing, and economics of carbon-based resources and of climate change issues. After this course, students are familiar with relevant technical and non-technical issues related to using carbon resources, climate change, and CCUS as a mitigation measure. The class will be structured in 2 hours of lecture and one hour of exercises/discussion. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The transition to a net-zero society is associated with major challenges in all sectors, including energy, transportation, and industry. In the IPCC Special Report on Global Warming of 1.5 °C, rapid emission reduction and negative emission technologies are crucial to limiting global warming to below 1.5 °C. Therefore, this course illuminates carbon capture, utilization, and storage as a potential set of technologies for emission mitigation and for generating negative emissions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture slides and supplementary documents will be available online. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | IPCC Special Report on Global Warming of 1.5°C, 2018. http://www.ipcc.ch/report/sr15/ IPCC AR6 Climate Change 2023: Synthesis Report, 2023. https://www.ipcc.ch/report/ar6/syr/ IPCC AR6 Climate Change 2022: Mitigation of Climate Change, 2022. https://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ Global Status of CCS 2020. Published by the Global CCS Institute, 2020. Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | External lecturers from the industry and other institutes will contribute with specialized lectures according to the schedule distributed at the beginning of the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 151-0206-00L | Energy Systems and Power Engineering | W | 4 KP | 2V + 2U | R. S. Abhari, A. Steinfeld | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Introductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | World primary energy resources and use: fossil fuels, renewable energies, nuclear energy; present situation, trends, and future developments. Sustainable energy system and environmental impact of energy conversion and use: energy, economy and society. Electric power and the electricity economy worldwide and in Switzerland; production, consumption, alternatives. The electric power distribution system. Renewable energy and power: available techniques and their potential. Cost of electricity. Conventional power plants and their cycles; state-of-the-art and advanced cycles. Combined cycles and cogeneration; environmental benefits. Solar thermal; concentrated solar power; solar photovoltaics. Fuel cells: characteristics, fuel reforming and combined cycles. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Vorlesungsunterlagen werden verteilt | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0254-00L | Environmental Aspects of Future Mobility | W | 4 KP | 2V + 1U | Y. Wright, P. Dimopoulos Eggenschwiler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course describes and assesses the environmental performance of current and future mobility/transportation and transformation pathways towards sustainability. It focuses in particular on the future role of renewable synthetic chemical energy carriers from a technology point of view. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students understand the systemic nature of current and future mobili-ty/transportation systems and are able to elaborate solutions for the defossiliza-tion of the sector. At the end of the course they should be capable to assess alter-native technologies for the different subsectors for transport of people and freight including the “upstream” energy supply processes for different energy carriers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Mobility system structure, future demand trends for the various sectors (people, freight, off-road, marine, aviation) and appropriate energy carriers per application. Basic characteristics of the currently most promising energy carrier concepts: Li-Ion Batteries, Hydrogen and synthetic fuels. Methods for producing renewable en-ergy carriers (electrolysis, methanation/synthesis of higher hydrocarbons etc.) and related infrastructure requirements. For internal combustion engines (ICE), which will continue to be used in sectors difficult to electrify (marine, off-road, heavy-duty long-haul freight transport), dif-ferent combustion modes and their respective pollutant emission formation mechanisms are presented and in-cylinder emission minimization methods for conventional and renewable fuels are discussed. Exhaust gas aftertreatment for combustion engines and atmospheric immissions are finally presented in view of near-zero emission powertrain concepts. Basic environmental assessment of the introduced concepts. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Due to the wide range of material covered, this course requires basics of thermo-dynamics/cycles, turbulent flows as well as combustion concepts (laminar and tur-bulent premixed and non-premixed flames). Ideally a combination of 151-0293-00L "Combustion and Reactive Processes in Energy and Materials Technology", where background on reactive processes is provided, and, 151-0251-00L "Princi-ples, efficiency optimization and future applications of IC engines", where thermo-dynamic cycles and combustion modes in internal combustion engines are dis-cussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 227-0664-00L | Technology and Policy of Electrical Energy Storage | W | 3 KP | 2G | V. Wood, T. Schmidt | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | With the global emphasis on decreasing CO2 emissions, achieving fossil fuel independence and growing the use of renewables, developing & implementing energy storage solutions for electric mobility & grid stabilization represent a key technology & policy challenge. This course uses lithium ion batteries as a case study to understand the interplay between technology, economics, and policy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students will learn of the complexity involved in battery research, design, production, as well as in investment, economics and policy making around batteries. Students from technical disciplines will gain insights into policy, while students from social science backgrounds will gain insights into technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | With the global emphasis on decreasing CO2 emissions, achieving fossil fuel independence, and integrating renewables on the electric grid, developing and implementing energy storage solutions for electric mobility and grid stabilization represent a key technology and policy challenge. The class will focus on lithium ion batteries since they are poised to enter a variety of markets where policy decisions will affect their production, adoption, and usage scenarios. The course considers the interplay between technology, economics, and policy. * intro to energy storage for electric mobility and grid-stabilization * basics of battery operation, manufacturing, and integration * intro to the role of policy for energy storage innovation & diffusion * discussion of complexities involved in policy and politics of energy storage | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Materials will be made available on the website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Materials will be made available on the website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Strong interest in energy and technology policy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Daten und Informationstechnologie | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-3900-00L | Big Data for Engineers This course is not intended for Computer Science and Data Science MSc students! | W | 6 KP | 2V + 2U + 1A | G. Fourny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course is part of the series of database lectures offered to all ETH departments, together with Information Systems for Engineers. It introduces the most recent advances in the database field: how do we scale storage and querying to Petabytes of data, with trillions of records? How do we deal with heterogeneous data sets? How do we deal with alternate data shapes like trees and graphs? | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | This lesson is complementary with Information Systems for Engineers as they cover different time periods of database history and practices -- you can even take both lectures at the same time. The key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations. This combination of requirements, together with the technologies that have emerged in order to address them, is typically referred to as "Big Data." This revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm". Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it were and are still needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small. The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof. After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each business use case efficiently and consistently. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe. It targets specifically students with a scientific or Engineering, but not Computer Science, background. We take the monolithic, one-machine relational stack from the 1970s, smash it down and rebuild it on top of large clusters: starting with distributed storage, and all the way up to syntax, models, validation, processing, indexing, and querying. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem. No data is harmed during this course, however, please be psychologically prepared that our data may not always be in normal form. - physical storage: distributed file systems (HDFS), object storage(S3), key-value stores - logical storage: document stores (MongoDB), column stores (HBase) - data formats and syntaxes (XML, JSON, RDF, CSV, YAML, protocol buffers, Avro) - data shapes and models (tables, trees) - type systems and schemas: atomic types, structured types (arrays, maps), set-based type systems (?, *, +) - an overview of functional, declarative programming languages across data shapes (SQL, JSONiq) - the most important query paradigms (selection, projection, joining, grouping, ordering, windowing) - paradigms for parallel processing, two-stage (MapReduce) and DAG-based (Spark) - resource management (YARN) - what a data center is made of and why it matters (racks, nodes, ...) - underlying architectures (internal machinery of HDFS, HBase, Spark) - optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing) - applications. Large scale analytics and machine learning are outside of the scope of this course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | https://ghislainfourny.github.io/big-data-textbook/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Papers from scientific conferences and journals. References will be given as part of the course material during the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This course is not intended for Computer Science and Data Science students. Computer Science and Data Science students interested in Big Data MUST attend the Master's level Big Data lecture, offered in Fall. Requirements: programming knowledge (Java, C++, Python, PHP, ...) as well as basic knowledge on databases (SQL). If you have already built your own website with a backend SQL database, this is perfect. Attendance is especially recommended to those who attended Information Systems for Engineers last Fall, which introduced the "good old databases of the 1970s" (SQL, tables and cubes). However, this is not a strict requirement, and it is also possible to take the lectures in reverse order. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 227-0558-00L | Principles of Distributed Computing | W | 7 KP | 2V + 2U + 2A | R. Wattenhofer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Distributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Distributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems. Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Lecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world. Mastering Distributed Algorithms Roger Wattenhofer Inverted Forest Publishing, 2020. ISBN 979-8628688267 Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Course pre-requisites: Interest in algorithmic problems. (No particular course needed.) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 363-1091-00L | Social Data Science | W | 2 KP | 3G | D. Garcia Becerra | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Social Data Science is introduced as a set of techniques to analyze human behaviour and social interaction through digital traces. The course focuses both on the fundamentals and applications of Data Science in the Social Sciences, including technologies for data retrieval, processing, and analysis with the aim to derive insights that are interpretable from a wider theoretical perspective. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | A successful participant of this course will be able to: - understand a wide variety of techniques to retrieve digital trace data from online data sources - store, process, and summarize online data for quantitative analysis - perform statistical analyses to test hypotheses, derive insights, and formulate predictions - interpret the results of data analysis with respect to theoretical and testable principles of human behavior - understand the limitations of observational data analysis with respect to data volume, statistical power, and external validity | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Social Data Science (SDS) provides a broad approach to the quantitative analysis of human behavior through digital trace data. SDS integrates the implementation of data retrieval and processing, the application of statistical analysis methods, and the interpretation of results to derive insights of human behavior at high resolutions and large scales. The motivation of SDS stems from theories in the Social Sciences, which are addressed with respect to societal phenomena and formulated as principles that can be tested against empirical data. Data retrieval in SDS is performed in an automated manner, accessing online databases and programming interfaces that capture the digital traces of human behavior. Data processing is computerized with calibrated methods that quantify human behavior, for example constructing social networks or measuring emotional expression. These quantities are used in statistical analyses to both test hypotheses and explore new aspects on human behavior. The course starts with an introduction to Social Data Science and the R statistical language, followed by three content blocks: collective behavior, sentiment analysis, and social network analysis. The course will cover various examples of the application of SDS: - Search trends to measure information seeking - Popularity and social impact - Evaluation of sentiment analysis techniques - Twitter social network analysis The lectures include theoretical foundations of the application of digital trace data in the Social Sciences, as well as practical examples of data retrieval, processing, and analysis cases in the R statistical language from a literate programming perspective. The block course contains lectures and exercise sessions during the morning and afternoon of five days. Exercise classes provide practical skills and discuss the solutions to exercises that build on the concepts and methods presented in the previous lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | The lecture slides will be available on the Moodle platform, for registered students only. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | See handouts. Specific literature is provided for download, for registered students only. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Participants of the course should have some basic background in statistics and programming, and an interest to learn about human behavior from a quantitative perspective. Prior knowledge of advanced R, information retrieval, or information systems is not necessary. Exercise sessions build on technical and theoretical content explained in the lectures. Students need a working laptop with Internet access to perform guided exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 252-0312-00L | Mobile Health and Activity Monitoring | W | 6 KP | 2V + 3A | C. Holz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Health and activity monitoring has become a key purpose of mobile & wearable devices (e.g., phones, watches, rings). We will cover the phenomena they capture, user behavior, activity, and human physiology, alongside the sensors, signals, and methods they leverage. In the exercise, students will process raw recordings from a wearable wristband to extract activity insights and health signals. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course will combine high-level concepts with low-level technical methods needed to sense, detect, and understand them. High-level: – sensing modalities for interactive systems – "activities" and "events" (exercises and other mechanical activities such as movements and resulting vibrations) – health monitoring (basic cardiovascular physiology) – affective computing (emotions, mood, personality) Lower-level: – sampling and filtering, time and frequency domains – cross-modal sensor systems, signal synchronization and correlation – event detection, classification, prediction using basic signal processing as well as learning-based methods – sensor types: optical, mechanical/acoustic, electromagnetic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Health and activity monitoring has become a key purpose of mobile and wearable devices, including phones, (smart) watches, (smart) rings, (smart) belts, and other trackers (e.g., shoe clips, pendants). In this course, we will cover the fundamental aspects that these devices observe, i.e., user behavior, actions, and physiological dynamics of the human body, as well as the sensors, signals, and methods to capture, process, and analyze them. We will then cover methods for pattern extraction and classification on such data. The course will therefore touch on aspects of human activities, cardiovascular and pulmonary physiology, affective computing (recognizing, interpreting, and processing emotions), corresponding lower-level sensing systems (e.g., inertial sensing, optical sensing, photoplethysmography, electrodermal activity, electrocardiograms) and higher-level computer vision-based sensing (facial expressions, motions, gestures), as well as processing methods for these types of data. The course will be accompanied by a group exercise project, in which students will apply the concepts and methods taught in class. Students will receive a wearable wristband device that streams IMU data to a mobile phone (code will be provided for receiving, storing, visualizing on the phone). Throughout the course and exercises, we will collect data of various human activities from the band, annotate them, analyze, classify, and interpret them. For this, existing and novel processing methods will be developed (plenty of related work exists), based on the collected data as well as existing datasets. We will also combine the band with signals obtained from the mobile phone to holistically capture and analyze health and activity data. Full details: https://siplab.org/courses/mobile_health_activity_monitoring/2024 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Copies of the slides will be made available. Related work and further reading will be provided. More information on the course site: https://siplab.org/courses/mobile_health_activity_monitoring/2024 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Will be provided in the lecture | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 252-0220-00L | Introduction to Machine Learning Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact the lecturers for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch | W | 8 KP | 4V + 2U + 1A | F. Perez Cruz, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course introduces the foundations of learning and making predictions based on data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | - Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Gesundheitswissenschaften und -technologie | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-0662-00L | Environmental Exposures (Air Pollution and Noise) and Health Effects | W | 3 KP | 2V | C.‑T. Monn, M. Brink | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Environmental exposures to air pollutants and noise and their effects on human health and well-being are discussed. Concepts and methods for exposure measurement and assessment are shown. In the first part of the lecture, air pollutants (e.g. fine particles and ozone) are dealt with. In the second part, noise exposure and its detrimental health effects stand in the foreground. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Understand the basic concepts of exposure assessment (air pollution, noise exposure). Understand exposure-response relationships between environmental pollutants and disease risks. Know the methods used in health effects research and environmental epidemiology. Know how air pollution and noise are treated legally and by which means these factors can be reduced. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Air Pollutants: - Sources of air pollutants - Fate in the atmosphere (dispersion, transformation etc.) - Indoor air pollution - Concepts of an exposure assessment - Concepts for setting air quality standards - Health effect of pollutants (e.g. from fine particles and ozone) Noise - Introduction to acoustics, measurement of sound - Hearing and auditory processing - Exposure assessment of noise - Noise effects, Exposure-effect relationships of noise - Basics of noise control and abatement, exposure limits - Noise abatement policy | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Presentation slides (ppt, pdf) and additional files will be made available online prior to individual lecture dates. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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