Search result: Catalogue data in Spring Semester 2019
GESS Science in Perspective Only the topics listed in this paragraph can be chosen as "GESS Science in Perspective" course. Further below you will find the "type B courses Reflections about subject specific methods and content" as well as the language courses. 6 ECTS need to be acquired during the BA and 2 ECTS during the MA Students who already took a course within their main study program are NOT allowed to take the course again. These course units are also listed under "Type A", which basically means all students can enroll | ||||||
Type B: Reflection About Subject-Specific Methods and Contents Subject-specific courses: Recommended for doctoral, master and bachelor students (after first-year examination only). Students who already took a course within their main study program are NOT allowed to take the course again. | ||||||
D-INFK | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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851-0585-38L | Data Science in Techno-Socio-Economic Systems Number of participants limited to 80 This course is thought be for students in the 5th semester or above with quantitative skills and interests in modeling and computer simulations. Particularly suitable for students of D-INFK, D-ITET, D-MAVT, D-MTEC, D-PHYS | W | 3 credits | 2V | E. Pournaras, T. Guo, D. Helbing | |
Abstract | This course introduces how techno-socio-economic systems in our nowadays digital society can be better understood with techniques and tools of data science. Students shall learn the fundamentals of data science, machine learning, but also advanced distributed real-time data analytics in the Planetary Nervous System. Students shall deliver and present a seminar thesis at the end of the course. | |||||
Learning objective | The goal of this course is to qualify students with knowledge on data science as a way to understand complex techno-socio-economic systems in our nowadays digital societies. This course aims to make students capable of applying the most appropriate and effective techniques of data science under different application scenarios. The course aims to engage students in exciting state-of-the-art scientific and collaboration platforms such as the Planetary Nervous System. The course shall increase the awareness level of students about the challenges and open issues of data science in socio-technical domains such as privacy. Finally students have the opportunity to develop their writing, presentation and collaboration skills based on a seminar thesis they have to deliver and present at the end of the course | |||||
851-0740-00L | Big Data, Law, and Policy Number of participants limited to 35 Students will be informed by 3.3.2019 at the latest. | W | 3 credits | 2S | S. Bechtold, T. Roscoe, E. Vayena | |
Abstract | This course introduces students to societal perspectives on the big data revolution. Discussing important contributions from machine learning and data science, the course explores their legal, economic, ethical, and political implications in the past, present, and future. | |||||
Learning objective | This course is intended both for students of machine learning and data science who want to reflect on the societal implications of their field, and for students from other disciplines who want to explore the societal impact of data sciences. The course will first discuss some of the methodological foundations of machine learning, followed by a discussion of research papers and real-world applications where big data and societal values may clash. Potential topics include the implications of big data for privacy, liability, insurance, health systems, voting, and democratic institutions, as well as the use of predictive algorithms for price discrimination and the criminal justice system. Guest speakers, weekly readings and reaction papers ensure a lively debate among participants from various backgrounds. | |||||
851-0732-03L | Intellectual Property: An Introduction Number of participants limited to 150 Particularly suitable for students of D-ARCH, D-BIOL, D-CHAB, D-INFK, D-ITET, D-MAVT, D- MATL, D-MTEC. | W | 2 credits | 2V | S. Bechtold, M. Schonger | |
Abstract | The course introduces students to the basics of the intellectual property system and of innovation policy. Areas covered include patent, copyright, trademark, design, know-how protection, open source, and technology transfer. The course looks at Swiss, European, U.S. and international law and uses examples from a broad range of technologies. Insights can be used in academia, industry or start-ups. | |||||
Learning objective | Intellectual property issues become more and more important in our society. In order to prepare students for their future challenges in research, industry or start-ups, this course introduces them to the foundations of the intellectual property system. The course covers patent, copyright, trademark, design, know-how protection, open source, and technology transfer law. It explains links to contract, antitrust, Internet, privacy and communications law where appropriate. While the introduction to these areas of the law is designed at a general level, examples and case studies come from various jurisdictions, including Switzerland, the European Union, the United States, and international law. In addition, the course introduces students to the fundamentals of innovation policy. After exposing students to the economics of intellectual property protection, the course asks questions such as: Why do states grant property rights in inventions? Has the protection of intellectual property gone too far? How do advances in biotechnology and the Internet affect the intellectual property system? What is the relationship between open source, open access and intellectual property? What alternatives to intellectual property protection exist? Knowing how the intellectual property system works and what kind of protection is available is useful for all students who are interested in working in academia, industry or in starting their own company. Exposing students to the advantages and disadvantages of the intellectual property system enables them to participate in the current policy discussions on intellectual property, innovation and technology law. The course will include practical examples and case studies as well as guest speakers from industry and private practice. | |||||
851-0727-01L | Telecommunications Law Particularly suitable for students of D-INFK, D-ITET | W | 2 credits | 2V | C. von Zedtwitz | |
Abstract | Introduction to the basics of Telecommunications Law for non-lawyers. The course deals with the legal regulations and principles that apply to telecom network operators and telecom service providers (fixed-line and mobile phone). | |||||
Learning objective | By analyzing the most relevant legal provisions for a telecom provider in Switzerland students will learn about the main concepts of Swiss law. No previous legal courses required. | |||||
Content | 1. History of Swiss Telecommunications Law 2. Regulation of network access (essential facility doctrine, types of access) 3. Universal Service 4. Phone service contracts (fixed line and mobile phone service) 5. Mobil communication radiation regulation 6. Telecommunication secrecy 7. SPAM-Avoidance | |||||
Lecture notes | The powerpoint slides presented in the course will be made availabe online. In addition, links to relevant legal decisions and regulations will be accessible on the course website. | |||||
Literature | No mandatory readings. | |||||
Prerequisites / Notice | Short written exam at the end of the semester (scope and materials to be defined during the course). | |||||
851-0734-00L | Information Security Law Particularly suitable for students of D-INFK, D-ITET | W | 2 credits | 2V | U. Widmer | |
Abstract | Introduction to Information Security Law for non-legal students respectively prospective decision-makers in companies and public authorities who will have to deal with information security issues (CIOs, COOs, CEOs). The lectures will focus on the legal aspects of the security of ICT infrastructures, including networks (Internet), and of the transported and processed information. | |||||
Learning objective | The objective is to understand the meaning and aims of information security and the legal framework, to become acquainted with legal instruments available to provide effective protection for infrastructures and sensitive legal assets and to present an analysis of possible legal loopholes and potential measures. No prior legal knowledge is required for those wishing to attend these lectures. | |||||
Content | The lectures will deal with industry-specific as well as cross-sector specific themes involving both technology and law from the areas of data protection law, computer crimes, statutory duties of confidentiality, telecommunication surveillance (Internet), electronic signatures, liability etc. | |||||
Lecture notes | The lectures will be accompanied by powerpoint slide presentations, downloadable before the lectures begin, or available as hard copy at the lectures themselves. | |||||
Literature | References to further literature sources will be given in the lectures. | |||||
851-0588-00L | Introduction to Game Theory Does not take place this semester. Number of participants limited to 400 Particularly suitable for students of D-INFK, D-MATH | W | 3 credits | 2V | H. Nax, D. Helbing | |
Abstract | This course introduces the foundations of game theory with a focus on its basic mathematical principles. It treats models of social interaction, conflict and cooperation, the origin of cooperation, and concepts of strategic decision making behavior. Examples, applications, theory, and the contrast between theory and empirical results are particularly emphasized. | |||||
Learning objective | Learn the fundamentals, models, and logic of thinking about game theory. Learn basic mathematical principles. Apply formal game theory models to strategic interaction situations and critically assess game theory's capabilities through a wide array of applications and experimental results. | |||||
Content | Game theory provides a unified mathematical language to study interactions amongst different types of individuals (e.g. humans, firms, nations, animals, etc.). It is often used to analyze situations involving conflict and/or cooperation. The course introduces the basic concepts of both non-cooperative and cooperative game theory (players, strategies, coalitions, rules of games, utilities, etc.) and explains the most prominent game-theoretic solution concepts (Nash equilibrium, sub-game perfection, Core, Shapley Value, etc.). We will also discuss standard extensions (repeated games, incomplete information, evolutionary game theory, signal games, etc.). In each part of the course, we focus on examples and on selected applications of the theory in different areas. These include analyses of cooperation, social interaction, of institutions and norms, social dilemmas and reciprocity as well as applications on strategic behavior in politics and between countries and companies, the impact of reciprocity, in the labor market, and some applications from biology. Game theory is also applied to control-theoretic problems of transport planning and computer science. As we present theory and applications, we will also discuss how experimental and other empirical studies have shown that human behavior in the real world often does not meet the strict requirements of rationality from "standard theory", leading us to models of "behavioural" and "experimental" game theory. By the end of the course, students should be able to apply game-theoretic in diverse areas of analysis including > controlling turbines in a wind park, > nations negotiating international agreements, > firms competing in markets, > humans sharing a common resource, etc. | |||||
Lecture notes | See literature below. In addition we will provide additional literature readings and publish the lecture slides directly after each lecture. | |||||
Literature | K Binmore, Fun and games, a text on game theory, 1994, Great Source Education SR Chakravarty, M Mitra and P Sarkar, A Course on Cooperative Game Theory, 2015, Cambridge University Press A Diekmann, Spieltheorie: Einführung, Beispiele, Experimente, 2009, Rowolth MJ Osborne, An Introduction to Game Theory, 2004, Oxford University Press New York J Nash, Non-Cooperative Games, 1951, Annals of Mathematics JW Weibull, Evolutionary game theory, 1997, MIT Press HP Young, Strategic Learning and Its Limits, 2004, Oxford University Press | |||||
851-0591-01L | BETH - Blockchain for Sustainability Number of participants limited to 200 Particularly suitable for students of D-INFK, D-MTEC, D-ITET, D-MAVT,D-PHYS | W | 3 credits | 4G | M. M. Dapp, D. Helbing, S. Klauser | |
Abstract | Blockchain and Internet of Things technologies hold the promise to transform our societies and economies. While IoT devices allow us to measure all kinds of activity by humans and machines, the blockchain allows us to securely time-stamp and value this data and even give it a price to trade it on (new) markets. We explore this potential with a specific focus on sustainable development. | |||||
Learning objective | The course provides opportunities to gain fundamental understanding of promising new technologies as well as develop creative decentralized solutions for societal challenges using these technologies. Participants will learn the fundamentals of blockchain technology, its mechanisms, design parameters and potential for decentralized solutions. Those with software development skills will then further explore the blockchain to develop hands-on decentralized applications and smart contracts. Non-coding participants will further explore how these technologies could be used to design new economic systems. These new cryptoeconomic systems should give citizens multiple incentives to increase cooperation, health, recycling, or education and other positive externalities and to decrease emissions, waste, noise, or stress and other negative externalities. During the hackathon, participants will work in mixed teams on concrete challenges addressing some of the pressing global challenges our societies face, like climate change, financial instability, energy, or mass migration, etc. The aim is to develop decentralized approaches towards a sustainable, sharing circular economy using blockchain and IoT technologies. Teams will produce a short report (about 10 pages), demonstrate their hackathon prototype based on blockchain technology (Ethereum platform) and present to a interdisciplinary jury on the last day. Throughout the course, participants will hone their critical thinking abilities by leaving their own discipline and discussing best approaches to solve global complex challenges in an international, multi-disciplinary setting with invited subject matter experts and peers from all around the world. We encourage students with no programming experience, who are interested in the potential of blockchain and IoT to address global challenges, to apply as well! | |||||
851-0739-01L | Building a Robot Judge: Data Science For the Law Particularly suitable for students of D-INFK, D-ITET, D-MTEC | W | 3 credits | 2V | E. Ash | |
Abstract | This course explores the automation of decisions in the legal system. We delve into the tools from natural language processing and machine learning needed to predict judge decision-making and ask whether it is possible -- or even desirable -- to build a robot judge. | |||||
Learning objective | Is a concept of justice what truly separates man from machine? Recent advances in data science have caused many people to reconsider their responses to this question. With expanding digitization of legal data and corpora, alongside rapid developments in natural language processing and machine learning, the prospect arises for automating legal decisions. Data science technologies have the potential to improve legal decisions by making them more efficient and consistent. The benefits to society from this automation could be significant. On the other hand, there are serious risks that automated systems could replicate or amplify existing legal biases and rigidities. This course introduces students to the data science tools that are unlocking legal materials for computational and scientific analysis. We begin with the problem of representing laws as data, with a review of techniques for featurizing texts, extracting legal information, and representing documents as vectors. We explore methods for measuring document similarity and clustering documents based on legal topics or other features. Visualization methods include word clouds and t-SNE plots for spatial relations between documents. We next consider legal prediction problems. Given the evidence and briefs in this case, how will a judge probably decide? How likely is a criminal defendant to commit another crime? How much additional revenue will this new tax law collect? Students will investigate and implement the relevant machine learning tools for making these types of predictions, including regression, classification, and deep neural networks models. We then use these predictions to better understand the operation of the legal system. Under what conditions do judges tend to make errors? Against which types of defendants do parole boards exhibit bias? Which jurisdictions have the most tax loopholes? In a semester project, student groups will conceive and implement a research design for examining this type of empirical research question. Some programming experience in Python is required, and some experience with text mining is highly recommended. | |||||
851-0158-15L | The Human Between Deficiency And Cyborg. Trans- And Posthumanistic Visions Particularly suitable for student of D-HEST, D-INFK, D-ITET Number of participants limited to 50 | W | 3 credits | 2S | K. Liggieri | |
Abstract | In our everyday life we are surrounded by automated, self-regulated machines (smartphones, prostheses, etc.), these techniques are part of our lives and without them a human existence is no longer conceivable. So man needs technology for his life and survival. But how does this technology change people? How and with which techniques does it optimize itself? | |||||
Learning objective | Apart from the important possibilities of biomedical healing, the question must be asked in the seminar how our view of "man" and "machine" changes. How did man and technology change each other in modern enhancement, in which man intervenes with the machine in himself? What happens when man and technology merge and create new bodies (cyborgs, etc.)? The seminar will be about an assessment of our modern idea of man and machine, which is changing through trans- and posthumanistic visions. To this end, historical and current debates on optimization are to be addressed. | |||||
851-0125-81L | How Free Are We? Philosophical Theories on Freedom and Determinism Particularly suitable for students of D-BIOL, D-HEST, D-INFK, D-CHAB, D-HEST, D-PHYS | W | 3 credits | 2G | L. Wingert | |
Abstract | We are praised for our achievements and blamed for our failures. It is presupposed that our doings are something that is up to us. "It is up to us" often expresses our attitude to treat us as free beings. But are we really free, hence responsible for our behavior? Or is our behaviour entrenched in conditions properly understood as deterministic ones? | |||||
Learning objective | ||||||
851-0144-19L | Philosophy of Time Does not take place this semester. Particularly suitable for students of D-BIOL, D-INFK, D-MATH, D-PHYS | W | 3 credits | 2V | to be announced | |
Abstract | This course provides an introduction to philosophical issues surrounding the concept of time. We will treat topics such as: the existence of past, present, and future; the possibility of time travel; the constitution of time consciousness and its possible neurophysiological counterparts; temporal biases in the conduct of our lives; responsibility to future and past generations. | |||||
Learning objective | By the end of the course students are able to describe and compare different theories and concepts of time (physical time, perceptual time, historical time ...). They are able to identify and examine issues concerning time as they occur in various philosophical subdisciplines - especially in philosophy of science, philosophy of mind, metaphysics, and ethics. Students are in a position to critically discuss and evaluate the repercussions of these issues in broader scientific and social contexts. Part of the course reflects on methods and contents from physics, neuroscience/cognitive science, and logic. | |||||
Content | Zeit ist eine fundamentale Dimension, in der wir uns sowohl als biologisch-physikalische wie auch als geistige Wesen bewegen. Zeit durchzieht unser Dasein in verschiedenen Erscheinungsformen – unter anderem als physikalische Zeit, als wahrgenommene Zeit, als gesellschaftlich-intersubjektive Zeit und als historische Zeit. Dementsprechend war und ist das Thema Zeit immer wieder der Gegenstand von grundlegenden Diskussionen in unterschiedlichen philosophischen Teildisziplinen – von Metaphysik über Wissenschaftsphilosophie und Philosophie des Geistes bis hin zu Philosophiegeschichtsschreibung und Ethik. Im Kurs werden die wichtigsten zeitspezifischen Fragestellungen dieser verschiedenen philosophischen Teildisziplinen und deren Querverbindungen behandelt. In diesem Sinne bietet der Kurs auch eine allgemeine Einführung in die Philosophie. Behandelt wird u.a.: die Existenz von Vergangenheit, Gegenwart und Zukunft; die Möglichkeit von Zeitreisen; die Konstitution unseres Zeitbewusstseins und dessen mögliche neurophysiologische Gegenstücke; zeitliche Vorurteile in unserer Lebensführung ("lieber heut' als morgen"); Verantwortung gegenüber zukünftigen und vergangenen Generationen. Die einzelnen Themen der Vorlesungen lauten und gliedern sich wie folgt: 1. Einleitung: Zeit als grundlegende Dimension des geistigen und körperlichen Lebens a) Diverse Erscheinungsformen von Zeit b) Themen und Motive der Vorlesung 2. Metaphysische Positionen und Probleme a) Grundbegriffe und Grundpositionen b) Ein Argument gegen die Realität von Zeit c) Gegenwart der Erfahrung d) Zeitfluss, Wandel und Kausalität e) Determinismus und Fatalismus 3. Die formale Struktur von Zeit: Philosophie der Mathematik und Informatik a) Zenons «Pfeil» und (wieder) Wandel b) Ist Zeit ein Kontinuum? c) Simulationen und Zeitreihenanalysen 4. Die konkrete Struktur der äußeren Zeit: Philosophie der Physik a) Gerichtetheit der physikalischen Zeit b) Bedingungen und Möglichkeiten von Zeitmessungen c) Zeitreisen und zyklische Zeiten 5. Zeit wahrnehmen: Philosophie des Geistes und der Kognitionswissenschaften a) Phänomenologie des inneren Zeitbewusstseins b) Hören als Zeitwahrnehmung, Musik als «Zeitkunst» c) Neurophänomenologie der Zeit d) Pathologien der Zeitwahrnehmung: Zeit und Leid 6. Zeitfragen der Lebensführung und Ethik: Praktische Philosophie a) «Lieber heute als morgen» – Zeitliche Vorurteile und Wohlergehen b) Sind Taten nur im Nachhinein zu bestrafen? – Einige moralische Erwägungen c) Verantwortung gegenüber anderen Generationen und personale Identität d) Ist Zeit «ein knappes Gut»? – Metaphern und demokrat. Entscheidungsprozesse 7. Zeitlichkeit in der Forschung: Geschichtsschreibung der Philosophie a) Sollte die Philosophie ihre Vergangenheit kennen? b) Geschichten von Begriffen, Ideen und Problemen 8. Schluss: Reprise und (De-)Synchronisationen a) Wiederkehrende Fragestellungen und Antwortansätze b) Taktungen, Strukturanalogien und Resonanzkatastrophen | |||||
Literature | Der Kurs orientiert sich wesentlich an folgender Monographie, deren Anschaffung empfohlen wird: - Sieroka, N. 2018. Philosophie der Zeit – Grundlagen und Perspektiven (Reihe C.H.Beck Wissen). München: Beck-Verlag (ISBN 978-3-4067-2787-0) 128 S., 9.95€ (Taschenbuch), 7.99€ (Kindle/ebook). Diverse weitere Literaturhinweise folgen in der Vorlesung. Zentrale Texte werden zudem auf einer Lehrplattform zum Herunterladen bereitgestellt. | |||||
851-0732-05L | Internet Privacy Number of participants limited to 25. Please send an email to the lecturer briefly explaining your educational background. An introduction session will take place from 9:00 until 11:30 am on February 21, 2019. Particularly suitable for students of D-INFK, D-MTEC. | W | 3 credits | 1S | A. Stremitzer | |
Abstract | This course focuses on privacy in the Internet. We discuss the proper scope of privacy protection in the Internet and different regulatory approaches in the US, the EU, and Switzerland. We will also explore strategies to enforce privacy in the Internet through innovative legal strategies and “enforcement bots” (automated enforcement algorithms). | |||||
Learning objective | The aim of the course is to 1) Introduce students to current regulatory approaches to Internet privacy (useful contextual knowledge especially for computer science students) 2) Introduce students to business models of Internet companies and their potential threats and benefits (useful contextual knowledge especially for computer science students) 3) Discuss policy questions surrounding Internet Privacy (stimulate critical thinking) 4) Explore the potential innovative enforcement ideas that combine technical/ legal techniques whose technical and legal viability has yet to be tested. There will be a number of experts giving short guest lectures introducing topics. These experts will also participate in subsequent discussions. This is a format where students witness and participate in the creation of new ideas and knowledge (students see science/scholarship in progress) The following guest lecturers (and potentially others) intend to participate: Florencia Marrotta-Wurgler (NYU Law School, Professor of Law, Co-Reporter, Third Restatement of Consumer Contracts, American Law Institute) Florent Thouvenin (UZH, Professor of Law, Information and Communication Law) David Basin (ETH, Professor of Computer Science, Information Security Group) | |||||
851-0739-02L | Building a Robot Judge: Data Science for the Law (Course Project) This is the optional course project for "Building a Robot Judge: Data Science for the Law." Please register only if attending the lecture course or with consent of the instructor. Some programming experience in Python is required, and some experience with text mining is highly recommended. | W | 2 credits | 2V | E. Ash | |
Abstract | Students investigate and implement the relevant machine learning tools for making legal predictions, including regression, classification, and deep neural networks models. | |||||
Learning objective | ||||||
Content | Students will investigate and implement the relevant machine learning tools for making legal predictions, including regression, classification, and deep neural networks models. We will use these predictions to better understand the operation of the legal system. In a semester project, student groups will conceive and implement a research design for examining this type of empirical research question. |
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