Suchergebnis: Katalogdaten im Herbstsemester 2020
GESS Wissenschaft im Kontext (Science in Perspective)  Nur die in diesem Abschnitt aufgelisteten Fächer können als "GESS Wissenschaft im Kontext" angerechnet werden. Weiter unten finden Sie die Kurse im Bereich "Typ B. Reflexion über fachspezifische Methoden und Inhalte" sowie den Bereich "Sprachkurse" Im Bachelorstudium sind 6 KP und im Masterstudium 2 KP zu erwerben. Studierende, die eine Lerneinheit bereits im Rahmen ihres Fachstudiums abgelegt haben, dürfen dieselbe Veranstaltung NICHT nochmals belegen! | ||||||
 Typ B: Reflexion über fachspezifische Methoden und InhalteFachspezifische Lerneinheiten. Empfohlen für Studierende ab der Basisprüfung im Bachelor- oder für Studierende im Master- oder Promotionsstudium. Studierende, die eine Lerneinheit bereits im Rahmen ihres Fachstudiums abgelegt haben, dürfen dieselbe Veranstaltung NICHT nochmals belegen! Diese Lerneinheiten sind alle auch unter "Typ A" aufgelistet, d.h. sie sind grundsätzlich für alle Studierenden belegbar. | ||||||
| D-INFK | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|---|
| 851-0252-01L | Human-Computer Interaction: Cognition and Usability  Number of participants limited to 35. Particularly suitable for students of D-ARCH, D-INFK, D-ITET | W | 3 KP | 2S | H. Zhao, C. Hölscher, S. Ognjanovic | |
| Kurzbeschreibung | This seminar introduces theory and methods in human-computer interaction and usability. Cognitive Science provides a theoretical framework for designing user interfaces as well as a range of methods for assessing usability (user testing, cognitive walkthrough, GOMS). The seminar will provide an opportunity to experience some of the methods in applied group projects. | |||||
| Lernziel | This seminar will introduce key topics, theories and methodology in human-computer interaction (HCI) and usability. Presentations will cover basics of human-computer interaction and selected topics like mobile interaction, adaptive systems, human error and attention. A focus of the seminar will be on getting to know evaluation techniques in HCI. Students form work groups that first familiarize themselves with a select usability evaluation method (e.g. user testing, GOMS, task analysis, heuristic evaluation, questionnaires or Cognitive Walkthrough). They will then apply the methods to a human-computer interaction setting (e.g. an existing software or hardware interface) and present the method as well as their procedure and results to the plenary. Active participation is vital for the success of the seminar, and students are expected to contribute to presentations of foundational themes, methods and results of their chosen group project. In order to obtain course credit a written essay / report will be required (details to be specified in the introductory session of the course). | |||||
| 851-0742-00L | Contract Design Particularly suitable for students of D-ARCH, D-BAUG, D-CHAB, DMATH, D-MTEC, D-INFK, D-MAVT Number of participants limited to 30. | W | 2 KP | 2G | A. Stremitzer, N. Atkinson | |
| Kurzbeschreibung | This course takes an engineering approach to contracting. It aims to bridge the gap between economic contract theory, contract law scholarship and the drafting of real world contracts. Students will apply insights from mechanism design and law to the design of incentive compatible contracts. | |||||
| Lernziel | This course takes an engineering approach to contracting, bridging the gap between economic contract theory, contract law scholarship, and the drafting of real world contracts. It consists in discussing the economics underlying business transactions and applying those concepts to focused case studies. Students will apply insights from mechanism design and law to the design of incentive compatible contracts in business transactions. Transactions are agreements between two or more parties that work together to create and allocate value. They can take a range of forms that include: the sale of an asset; the formation and running of a business; initial public offerings (IPOs); debt financings; buyouts; sales out of bankruptcy; leases; construction contracts; oil & gas production contracts, movie financing deals, etc. Deals occur, and value is created, when deal professionals design structures that provide good incentives for all parties involved and constrain opportunities for future misbehavior. The class consists of three modules: Module 1: Contract Theory & Contract Design: The first part of the class consists in theoretical lectures aimed at equipping students with heuristic tools on how to write contracts. To this end, students learn about key concepts of economic and behavioral contract theory. Module 2: Drafting Contracts: The second part of the class initiates students to contract drafting, by analyzing and marking up real world contracts. Module 3: Structuring a Complex Contract for a (hypothetical) client organization: The third part of the class will subdivide the class into groups. Each group will be presented with a complex real world deal or case study. The students will then perform the following tasks: 1) Reconstruction of the economic and informational environment in which the contract was written. 2) Identification of the main economic, technical and legal challenges of the transaction. 3) Drafting of a strategic term sheet aimed at addressing those challenges. 4) Recommendations on how the actual contract can be improved. | |||||
| Voraussetzungen / Besonderes | The course is open to ETH students through the Science in Perspective Program of the Department of Humanities, Social and Political Sciences. This course has technical aspects that ETH students will be prepared for. UZH students must send a CV and a short letter of motivation to ensure that they have sufficient preparation for the course. Please email these materials to Dr. Atkinson (natkinson@ethz.ch) with the subject line "Contract Design Course", before the course begins. | |||||
| 851-0727-02L | E-Business-Recht Besonders geeignet für Studierende D-INFK, D-ITET | W | 2 KP | 2V | D. Rosenthal | |
| Kurzbeschreibung | Die Vorlesung befasst sich mit rechtlichen Rahmenbedingungen im elektronischen Geschäftsverkehr und der Informationstechnologie. Es werden diverse juristische Grundregeln und Konzepte erörtert, die in der Praxis zu beachten sind, sei es bei der Konzipierung von New-Media-Geschäftsmodellen, sei es in der Durchführung von Online-Aktivitäten und dem Einsatz von Informationstechnologien. | |||||
| Lernziel | Lernziel ist die Kenntnis und das Verständnis wichtiger rechtlicher Konzepte im Bereich des E-Business, so insbesondere das Verständnis wie E-Business durch das Recht national und international überhaupt erfasst wird, wie Verträge auf elektronischem Wege geschlossen und abgewickelt werden können, welche Regeln insbesondere im Internet beim Umgang mit fremden und eigenen Inhalten und Kundendaten zu beachten sind, wer im E-Business wofür haften muss und welche Rolle das Recht beim praktischen Aufbau und Betrieb von E-Business-Anwendungen spielt. | |||||
| Inhalt | Vorgesehene Strukturierung der Vorlesung: 1) Welches Recht gilt im E-Business? –Internationalität des Internets –Regulierte Branchen 2) Gestaltung und Vermarktung von E-Business-Angeboten Verwendung fremder und Schutz der eigenen Inhalte –Haftung im E-Business (und wie sie beschränkt werden kann) –Domain-Namen 3) Beziehung zu E-Business-Kunden –Verträge im E-Business, Konsumentenschutz –Elektronische Signaturen –Datenschutz Spam 4) Verträge mit E-Business-Providern Änderungen, Umstellungen und Kürzungen bleiben vorbehalten. Der aktuelle Termin- und Themenplan ist zu gegebener Zeit über die elektronische Dokumentenablage abrufbar ( https://ilias-app2.let.ethz.ch/goto.php?target=crs_157989&client_id=ilias_lda). | |||||
| Skript | Es wird mit Folien gearbeitet, die als PDF über die elektronische Dokumentenablage (ILIAS) auf dem System der ETHZ abrufbar sind. Auf dem Termin- und Themenplan (ebenfalls online abrufbar) sind Links zu Gesetzestexten und weiteren Unterlagen abrufbar. COVID-19-bedingt erfolgt die Vorlesung ausschliesslich online, d.h. es wird ein Podcast zum Download angeboten (der genaue Ort wird noch bekanntgegeben). Der Termin- und Themenplan ist zu gegebener Zeit über die elektronische Dokumentenablage abrufbar ( https://ilias-app2.let.ethz.ch/goto.php?target=crs_195175&client_id=ilias_lda ). | |||||
| Literatur | Weiterführende Materialien, Links und Literatur sind auf dem Termin- und Themenplan aufgeführt (zu gegebener Zeit abrufbar via elektronische Dokumentenablage, https://ilias-app2.let.ethz.ch/goto.php?target=crs_195175&client_id=ilias_lda ). | |||||
| Voraussetzungen / Besonderes | Die Semesterendprüfung findet üblicherweise in Form eines schriftlichen Kurztests (normalerweise MC) in der letzten Doppelstunde statt. Es wird angegeben, welche Unterlagen beim jeweiligen Thema den Prüfungsstoff definieren. Wie dies im Rahmen von COVID-19 geschehen wird, wird noch geklärt. Der Test wird möglicherweise elektronisch durchgeführt. | |||||
| 851-0738-00L | Geistiges Eigentum: Eine Einführung Besonders geeignet für Studierende D-CHAB, D-INFK, D-ITET, D-MAVT, D- MATL, D-MTEC | W | 2 KP | 2V | M. Schweizer | |
| Kurzbeschreibung | Die Vorlesung bietet eine Einführung in das schweizerische und europäische Immaterialgüterrecht (Marken-, Urheber-, Patent- und Designrecht). Auch werden die Aspekte des Wettbewerbsrechts behandelt, die für den Schutz geistiger Schöpfungen und unternehmens- oder produktbezogener Zeichen relevant sind. Die rechtlichen Grundlagen werden anhand aktueller Fälle erarbeitet. | |||||
| Lernziel | Ziel der Vorlesung ist es, ETH-Studierende in die Lage zu versetzen, zu erkennen, welche Schutzrechte die von ihnen geschaffenen Leistungen möglicherweise schützen oder verletzen können. Dadurch lernen die Studierenden, die immaterialgüterrechtlichen Chancen und Risiken bei der Entwicklung und Vermarktung von Produkten abzuschätzen. Dazu müssen sie die Schutzvoraussetzungen und den Schutzumfang der verschiedenen immaterialgüterrechtlichen Schutzrechte ebenso kennen wie die Probleme, die typischerweise bei der Durchsetzung von Schutzrechten auftreten. Diese Kenntnisse sollen praxisnah aufgrund von aktuellen Urteilen und Fällen vermittelt werden. Ein weiteres Ziel ist es, den Studierenden zu ermöglichen, informiert an der aktuellen Diskussion über die Ziele und Wünschbarkeit des Schutzes geistiger Leistungen teilzunehmen, wie sie insbesondere auf den Gebieten des Urheberrechts (Stichworte fair use, Creative Commons, Copyleft) und Patentrechts (Software-Patente, patent trolls, patent thickets), geführt wird. | |||||
| 851-0252-13L | Network Modeling Particularly suitable for students of D-INFK Students are required to have basic knowledge in inferential statistics, such as regression models. | W | 3 KP | 2V | C. Stadtfeld, V. Amati | |
| Kurzbeschreibung | Network Science is a distinct domain of data science that focuses on relational systems. Various models have been proposed to describe structures and dynamics of networks. Statistical and numerical methods have been developed to fit these models to empirical data. Emphasis is placed on the statistical analysis of (social) systems and their connection to social theories and data sources. | |||||
| Lernziel | Students will be able to develop hypotheses that relate to the structures and dynamics of (social) networks, and tests those by applying advanced statistical network methods such as exponential random graph models (ERGMs) and stochastic actor-oriented models (SAOMs). Students will be able to explain and compare various network models, and develop an understanding of how those can be fit to empirical data. This will enable students to independently address research questions from various social science fields. | |||||
| Inhalt | The following topics will be covered: - Introduction to network models and their applications - Stylized models: * uniform random graph models * small world models * preferential attachment models - Models for testing hypotheses while controlling for the network structure: *Quadratic assignment procedure regression (QAP regression) - Models for testing hypotheses on the network structure: * Models for one single observation of a network: exponential random graph models (ERGMs) * Models for panel network data: stochastic actor-oriented models (SAOMs) * Models for relational event data: dynamic network actor models (DyNAMs) The application of these models is illustrated through examples and practical sessions involving the analysis of network data using the software R. | |||||
| Skript | Slides and lecture notes are distributed via the associated course moodle. | |||||
| Literatur | - Krackardt, D. (1987). QAP partialling as a test of spuriousness. Social networks, 9(2), 171-186. - Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social networks, 29(2), 173-191. - Snijders, T. A. B., Van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social networks, 32(1), 44-60. - Snijders, T. A. B. (2011). Statistical models for social networks. Annual Review of Sociology, 37. - Stadtfeld, C., & Block, P. (2017). Interactions, actors, and time: Dynamic network actor models for relational events. Sociological Science, 4, 318-352. | |||||
| Voraussetzungen / Besonderes | Students are required to have basic knowledge in inferential statistics and should be familiar with linear and logistic regression models. | |||||
| 851-0252-15L | Network Analysis Particularly suitable for students of D-INFK, D-MATH | W | 3 KP | 2V | U. Brandes | |
| Kurzbeschreibung | Network science is a distinct domain of data science that is characterized by a specific kind of data being studied. While areas of application range from archaeology to zoology, we concern ourselves with social networks for the most part. Emphasis is placed on descriptive and analytic approaches rather than theorizing, modeling, or data collection. | |||||
| Lernziel | Students will be able to identify and categorize research problems that call for network approaches while appreciating differences across application domains and contexts. They will master a suite of mathematical and computational tools, and know how to design or adapt suitable methods for analysis. In particular, they will be able to evaluate such methods in terms of appropriateness and efficiency. | |||||
| Inhalt | The following topics will be covered with an emphasis on structural and computational approaches and frequent reference to their suitability with respect to substantive theory: * Empirical Research and Network Data * Macro and Micro Structure * Centrality * Roles * Cohesion | |||||
| Skript | Lecture notes are distributed via the associated course moodle. | |||||
| Literatur | * Hennig, Brandes, Pfeffer & Mergel (2012). Studying Social Networks. Campus-Verlag. * Borgatti, Everett & Johnson (2013). Analyzing Social Networks. Sage. * Robins (2015). Doing Social Network Research. Sage. * Brandes & Erlebach (2005). Network Analysis. Springer LNCS 3418. * Wasserman & Faust (1994). Social Network Analysis. Cambridge University Press. * Kadushin (2012). Understanding Social Networks. Oxford University Press. | |||||
| 851-0732-06L | Law & Tech Number of participants limited to 30. | W | 3 KP | 3S | A. Stremitzer, J. Merane, A. Nielsen | |
| Kurzbeschreibung | This course introduces students to legal, economic, and social perspectives on the increasing economic and social importance of technology. We focus particularly on the challenges to current law posed by the increasing rate of tech innovation and adoption generally and also by case-specific features of prominent near-future technologies. | |||||
| Lernziel | The course is intended for a wide range of engineering students, from machine learning to bioengineering to human computer interaction, as well as for law students interested in acquiring a better understanding of state-of-the-art technology. The course will combine both an overview of major areas of law that affect the regulation of technology and also guest lectures on the state-of-the art in a variety of important technologies, ranging from autonomous vehicles to fair artificial intelligence to consumer-facing DNA technologies. The course is open to ETH students through the Science in Perspective program of the Department of Humanities, Social and Political Sciences. | |||||
| Inhalt | The planned course outline is below 1. Overview of science, law, and technology a. Studies of law and technology b. Should science be regulated, and if so, how? c. Technology as a social problem 2. Designing technology for humans a. Attention fiduciaries and the digital environment b. Does technology weaponize known problems of bounded human rationality? c. Should technology be regulated as a psychotropic substance? An addictive substance? d. Can technology make life easier? e. Psychological effects of surveillance 3. Governing tech a. Can small governments regulate big tech? b. National and supranational legislation c. Enforcing the law with technology d. Can enforcement be baked into technology? 4. AI and fairness a. Discrimination b. Privacy c. Opacity d. AI and due process 5. Trade secret and technological litigation a. Trade secret is a long-standing tool for litigation but does it enjoy too much deference? b. Trade secrets and the rights of employes 6. Enforcement against tech a. Big tech and antitrust b. Consumer protection 7. The Digital Battlefield a. Technology for spying b. Spying on technology companies c. Race to be AI superpower d. Immigration policy 8. Contract law a. Smart contracts b. Modernizing contract law and practice c. Regulating cryptocurrencies 9. Tort law a. Applying existing tort law to new autonomous technologies b. Personhood and personal responsibility c. Victim entitlements 10. Self-driving cars and other autonomous robotics a. Legal regimes b. Diversity in morality judgements related to autonomous vehicles 11. Biometrics a. Widespread use of facial recognition b. Law enforcement c. Connecting biometrics to social data d. Solving crimes with biometrics 12. New Biology and Medicine a. Unregulated science (biohackers) b. Promising technology before it can be delivered c. Connecting medicine to social data d. Using technology to circumvent medical regulations | |||||
| 851-0101-86L | Complex Social Systems: Modeling Agents, Learning, and Games Number of participants limited to 100. Prerequisites: Basic programming skills, elementary probability and statistics. | W | 3 KP | 2S | N. Antulov-Fantulin, D. Helbing | |
| Kurzbeschreibung | This course introduces mathematical and computational models to study techno-socio-economic systems and the process of scientific research. Students develop a significant project to tackle techno-socio-economic challenges in application domains of complex systems. They are expected to implement a model and communicating their results through a seminar thesis and a short oral presentation. | |||||
| Lernziel | The students are expected to know a programming language and environment (Python, Java or Matlab) as a tool to solve various scientific problems. The use of a high-level programming environment makes it possible to quickly find numerical solutions to a wide range of scientific problems. Students will learn to take advantage of a rich set of tools to present their results numerically and graphically. The students should be able to implement simulation models and document their skills through a seminar thesis and finally give a short oral presentation. | |||||
| Inhalt | Students are expected to implement themselves models of various social processes and systems, including agent-based models, complex networks models, decision making, group dynamics, human crowds, or game-theoretical models. Part of this course will consist of supervised programming exercises. Credit points are finally earned for the implementation of a mathematical or empirical model from the complexity science literature and the documentation in a seminar thesis. | |||||
| Skript | The lecture slides will be presented on the course web page after each lecture. | |||||
| Literatur | Agent-Based Modeling https://link.springer.com/chapter/10.1007/978-3-642-24004-1_2 Social Self-Organization https://www.springer.com/gp/book/9783642240034 Traffic and related self-driven many-particle systems Reviews of Modern Physics 73, 1067 https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.73.1067 An Analytical Theory of Traffic Flow (collection of papers) https://www.researchgate.net/publication/261629187 Pedestrian, Crowd, and Evacuation Dynamics https://www.research-collection.ethz.ch/handle/20.500.11850/45424 The hidden geometry of complex, network-driven contagion phenomena (relevant for modeling pandemic spread) https://science.sciencemag.org/content/342/6164/1337 Further literature will be recommended in the lectures. | |||||
| Voraussetzungen / Besonderes | The number of participants is limited to the size of the available computer teaching room. The source code related to the seminar thesis should be well enough documented. Good programming skills and a good understanding of probability & statistics and calculus are expected. | |||||
| 851-0171-00L | Images of Language | W | 3 KP | 1V + 1U | J. L. Gastaldi | |
| Kurzbeschreibung | Students will be made acquainted with the understanding of the conception and practice of language in different fields of knowledge, and how they are being transformed in the context of new digital practices. The lectures will be given by members of ETH with different disciplinary backgrounds, such as computer science, architecture, physics, history and literary studies. | |||||
| Lernziel | By the end of the course, students will be able to describe and compare different conceptions of languages at work in multiple scientific fields. They will be able to evaluate both the differences and the convergences between those conceptions. Students will also be in a position to critically assess the simultaneous effect of contemporary digital practices in the organization of all the fields of knowledge covered by the course. | |||||
| Inhalt | Students will be made acquainted with the understanding of the conception and practice of language in different fields of knowledge, and how it is being transformed in the context of new digital practices. Various members of ETH (with different disciplinary backgrounds) will present what they take to be crucial concepts, methods, challenges, and limits in our investigations of, for instance, natural language, the language and communication of living organisms, the forms of architecture, the physics of information, cryptography, the language of administration and literary studies. | |||||
| 851-0467-00L | From Traffic Modeling to Smart Cities and Digital Democracies Number of participants limited to 30. | W | 3 KP | 2S | D. Helbing, S. Mahajan | |
| Kurzbeschreibung | This seminar will present speakers who discuss the challenges and opportunities arisinig for our cities and societies with the digital revolution. Besides discussing questions of automation using Big Data, AI and other digital technologies, we will reflect on the question of how democracy could be digitally upgraded to promote innovation, sustainability, and resilience. | |||||
| Lernziel | To collect credit points, students will have to give a 30-40 minute presentation in the seminar, after which the presentation will be discussed. The presentation will be graded. | |||||
| Inhalt | This seminar will present speakers who discuss the challenges and opportunities arisinig for our cities and societies with the digital revolution. Besides discussing questions of automation using Big Data, AI and other digital technologies, we will also reflect on the question of how democracy could be digitally upgraded, and how citizen participation could contribute to innovation, sustainability, resilience, and quality of life. This includes questions around collective intelligence and digital platforms that support creativity, engagement, coordination and cooperation. | |||||
| Literatur | Martin Treiber and Arne Kesting Traffic Flow Dynamics: Data, Models and Simulation Link Dirk Helbing Traffic and related self-driven many-particle systems Reviews of Modern Physics 73, 1067 https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.73.1067 Dirk Helbing An Analytical Theory of Traffic Flow (collection of papers) https://www.researchgate.net/publication/261629187 Michael Batty, Kay Axhausen et al. Smart cities of the future Books by Michael Batty https://link.springer.com/article/10.1140/epjst/e2012-01703-3 How social influence can undermine the wisdom of crowd effect https://www.pnas.org/content/108/22/9020 Evidence for a collective intelligence factor in the performance of human groups https://science.sciencemag.org/content/330/6004/686.full Optimal incentives for collective intelligence https://www.pnas.org/content/114/20/5077.short Collective Intelligence: Creating a Prosperous World at Peace Link Big Mind: How Collective Intelligence Can Change Our World https://www.amazon.com/Big-Mind-Collective-Intelligence-Change/dp/0691170797/ Programming Collective Intelligence Link Urban architecture as connective-collective intelligence. Which spaces of interaction? https://www.mdpi.com/2071-1050/5/7/2928 Build digital democracy https://www.nature.com/news/society-build-digital-democracy-1.18690 How to make democracy work in the digital age Link Digital Democracy: How to make it work? http://futurict.blogspot.com/2020/06/digital-democracy-how-to-make-it-work.html Proof of witness presence: Blockchain consensus for augmented democracy in smart cities https://www.sciencedirect.com/science/article/pii/S0743731520303282 Iterative Learning Control for Multi-agent Systems Coordination Link Decentralized Collective Learning for Self-managed Sharing Economies https://dl.acm.org/doi/abs/10.1145/3277668 Further literature will be recommended in the lectures. | |||||
| 851-0172-00L | Around 1936: The New Language of Science Findet dieses Semester nicht statt. Number of participants limited to 35. | W | 3 KP | 2S | ||
| Kurzbeschreibung | The years around 1936 witnessed an intense intellectual production in all fields of knowledge. All those contributions had a common denominator: the reorganization of their fields around a formal conception of language, which changed our linguistic practices both in science and in everyday life. This seminar proposes a comparative reading of those texts, to understand that transformation. | |||||
| Lernziel | During the seminar, students will be able to: ⁃ Acquire a broad interdisciplinary perspective on the history of formal languages ⁃ Obtain philosophical and historical tools for critically assessing the status language and sign systems in scientific practices ⁃ Develop a critical understanding of the notion of formal ⁃ Discuss the methodological capabilities of historical epistemology | |||||
| Inhalt | The years around 1936 (say, between 1934 and 1938) were the occasion of an intense and fertile intellectual production, opening new and long-lasting perspectives in practically all fields of knowledge, from mathematics and physics to linguistics and aesthetics, and even inaugurating or prefiguring new disciplines such as computability, complexity or information theory. Indeed, within those few years, famous seminal papers and works appeared by authors such as Einstein, Turing, Church, Gödel, Kolmogorov, Bourbaki, Gentzen, Tarski, Carnap, Shannon, Hjelmslev, Schoenberg or Le Corbusier. Despite the diversity of fields of knowledge concerned by this intense production, all those contributions seem to have a common denominator. In essence, they all concern a reorganization of their respective fields around a new conception of language as being of a purely formal nature. In hindsight, it can be said this simultaneous intellectual effort ended up changing our conception and practice of language, of what it means to read and write, both in science and in everyday life. However, although simultaneous, those efforts were not necessarily convergent. Multiple tensions, incompatibilities and fragile alliances accompanied the emergence of orientations such as computability theory, complexity theory, structuralist mathematics, proof and model theory, logicism, information theory, structuralist linguistics or aesthetical formalism and constructivism. This seminar proposes, then, to perform a comparative reading of those original texts, to understand the nature of that transformation, the convergences and divergences between the different projects at stake, and how the singular way in which they have historically articulated still determines our contemporary practices and conceptions of language. | |||||
| 851-0098-00L | Wer und was ist vernünftig? Über Vernunft, KI und die Rolle von Wissenschaft in der Gesellschaft | W | 3 KP | 2G | L. Wingert | |
| Kurzbeschreibung | Technische Entwicklungen und politische Konflikte werfen die Frage auf: Wer und was ist vernünftig? Sind Roboter vernünftig oder nur verlässlich? Gilt Vernunft = Intelligenz? Sind Expertinnen, z.B. Klimaforscherinnen vernünftiger als das Volk? Sollten sie deshalb mehr politischen Einfluss haben? Für Antworten darauf sollen u.a. die Begriffe Vernunft und Intelligenz philosophisch geklärt werden. | |||||
| Lernziel | Teilnehmer sollte nach dem Besuch des Kurses Folgendes erreicht haben: 1. die Kenntnis von wichtigen Theorien der Vernunft und der Intelligenz und des Unterschiedes zwischen Intelligenz und Vernunft; 2. ein Verständnis, in welchem Sinn Roboter und Tiere intelligent sein können; 3. eine Einschätzung, was die Rolle von wissenschaftlichen Expertinnen in der Gesellschaft sein sollte. | |||||
| 851-0760-00L | Building a Robot Judge: Data Science for Decision-Making Particularly suitable for students of D-INFK, D-ITET, D-MTEC | W | 3 KP | 2V | E. Ash | |
| Kurzbeschreibung | This course explores the automation of decisions in the legal system. We delve into the machine learning tools needed to predict judge decision-making and ask whether techniques in model explanation and algorithmic fairness are sufficient to address the potential risks. | |||||
| Lernziel | This course introduces students to the data science tools that may provide the first building blocks for a robot judge. While building a working robot judge might be far off in the future, some of the building blocks are already here, and we will put them to work. | |||||
| Inhalt | Data science technologies have the potential to improve legal decisions by making them more efficient and consistent. On the other hand, there are serious risks that automated systems could replicate or amplify existing legal biases and rigidities. Given the stakes, these technologies force us to think carefully about notions of fairness and justice and how they should be applied. The focus is on 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? Students will be introduced to emerging applied research in this vein. In a semester paper, students (individually or in groups) will conceive and implement an applied data-science research project. | |||||
| 851-0761-00L | Building a Robot Judge: Data Science for Decision-Making (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 KP | 2V | E. Ash | |
| Kurzbeschreibung | Students investigate and implement the relevant machine learning tools for making legal predictions, including regression, classification, and deep neural networks models. This is the extra credit for a larger course project for the course. | |||||
| Lernziel | In a semester paper, students (individually or in groups) will conceive and implement their own research project applying natural language tools to legal texts. Some programming experience in Python is required, and some experience with NLP is highly recommended. | |||||
| Inhalt | 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. | |||||
| 851-0125-65L | A Sampler of Histories and Philosophies of Mathematics Besonders geeignet für Studierende D-CHAB, D-INFK, D-ITET, D-MATH, D-PHYS | W | 3 KP | 2V | R. Wagner | |
| Kurzbeschreibung | This course will review several case studies from the ancient, medieval and modern history of mathematics. The case studies will be analyzed from various philosophical perspectives, while situating them in their historical and cultural contexts. | |||||
| Lernziel | The course aims are: 1. To introduce students to the historicity of mathematics 2. To make sense of mathematical practices that appear unreasonable from a contemporary point of view 3. To develop critical reflection concerning the nature of mathematical objects 4. To introduce various theoretical approaches to the philosophy and history of mathematics 5. To open the students' horizons to the plurality of mathematical cultures and practices | |||||
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