Search result: Catalogue data in Autumn Semester 2020

GESS Science in Perspective Information
Only the topics listed in this paragraph can be chosen as GESS Science in Perspective.
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.
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.

These course units are also listed under "Type A", which basically means all students can enroll
D-INFK
NumberTitleTypeECTSHoursLecturers
851-0252-01LHuman-Computer Interaction: Cognition and Usability Restricted registration - show details
Number of participants limited to 35.

Particularly suitable for students of D-ARCH, D-INFK, D-ITET
W3 credits2SH. Zhao, C. Hölscher, S. Ognjanovic
AbstractThis 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.
ObjectiveThis 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).
851-0742-00LContract Design Restricted registration - show details
Particularly suitable for students of D-ARCH, D-BAUG, D-CHAB, DMATH, D-MTEC, D-INFK, D-MAVT

Number of participants limited to 30.
W2 credits2GA. Stremitzer, N. Atkinson
AbstractThis 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.
ObjectiveThis 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.
Prerequisites / NoticeThe 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 (Link) with the subject line "Contract Design Course", before the course begins.
851-0727-02LE-Business-Law
Particularly suitable for students of D-INFK, D-ITET
W2 credits2VD. Rosenthal
AbstractThe course deals with the basic legal framework for doing e-business as well as using information technology. It discusses a variety of legal concepts and rules to be taken into account in practice, be it when designing and planning new media business models, be it when implementing online projects and undertaking information technology activities.
ObjectiveThe objective is knowing and understanding key legal concepts relevant for doing e-business, in particularly understanding how e-business is regulated by law nationally and internationally, how contracts are concluded and performed electronically, which rules have to be obeyed in particular in the Internet with regard to third party and own content and client data, the concept of liability applied in e-business and the role of the law in the practical implementation and operation of e-business applications.
ContentVorgesehene 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 (
Link).
Lecture notesEs 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 (
Link ).
LiteratureWeiterführende Materialien, Links und Literatur sind auf dem Termin- und Themenplan aufgeführt (zu gegebener Zeit abrufbar via elektronische Dokumentenablage,
Link ).
Prerequisites / NoticeDie 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.
851-0738-00LIntellectual Property: Introduction
Particularly suitable for students of D-CHAB, D-INFK, D-ITET, D-MAVT, D- MATL, D-MTEC
W2 credits2VM. Schweizer
AbstractThe course provides an introduction to Swiss and European intellectual property law (trademarks, copyright, patent and design rights). Aspects of competition law are treated insofar as they are relevant for the protection of intellectual creations and source designations. The legal principles are developed based on current cases.
ObjectiveThe aim of this course is to enable students at ETH Zurich to recognize which rights may protect their creations, and which rights may be infringed as a result of their activities. Students should learn to assess the risks and opportunities of intellectual property rights in the development and marketing of new products. To put them in this position, they need to know the prerequisites and scope of protection afforded by the various intellectual property rights as well as the practical difficulties involved in the enforcement of intellectual property rights. This knowledge is imparted based on current rulings and cases.

Another goal is to enable the students to participate in the current debate over the goals and desirability of protecting intellectual creations, particularly in the areas of copyright (keywords: fair use, Creative Commons, Copyleft) and patent law (software patents, patent trolls, patent thickets).
851-0252-13LNetwork Modeling
Particularly suitable for students of D-INFK

Students are required to have basic knowledge in inferential statistics, such as regression models.
W3 credits2VC. Stadtfeld, V. Amati
AbstractNetwork 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.
ObjectiveStudents 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.
ContentThe 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.
Lecture notesSlides and lecture notes are distributed via the associated course moodle.
Literature- 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.
Prerequisites / NoticeStudents are required to have basic knowledge in inferential statistics and should be familiar with linear and logistic regression models.
851-0252-15LNetwork Analysis
Particularly suitable for students of D-INFK, D-MATH
W3 credits2VU. Brandes
AbstractNetwork 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.
ObjectiveStudents 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.
ContentThe 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
Lecture notesLecture notes are distributed via the associated course moodle.
Literature* 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.
851-0732-06LLaw & Tech Restricted registration - show details
Number of participants limited to 30.
W3 credits3SA. Stremitzer, J. Merane, A. Nielsen
AbstractThis 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.
ObjectiveThe 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.
ContentThe 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
851-0101-86LComplex Social Systems: Modeling Agents, Learning, and Games Information Restricted registration - show details
Number of participants limited to 100.

Prerequisites: Basic programming skills, elementary probability and statistics.
W3 credits2SN. Antulov-Fantulin, D. Helbing
AbstractThis 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.
ObjectiveThe 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.
ContentStudents 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.
Lecture notesThe lecture slides will be presented on the course web page after each lecture.
LiteratureAgent-Based Modeling
Link

Social Self-Organization
Link

Traffic and related self-driven many-particle systems
Reviews of Modern Physics 73, 1067
Link

An Analytical Theory of Traffic Flow (collection of papers)
Link

Pedestrian, Crowd, and Evacuation Dynamics
Link

The hidden geometry of complex, network-driven contagion phenomena (relevant for modeling pandemic spread)
Link

Further literature will be recommended in the lectures.
Prerequisites / NoticeThe 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.
851-0171-00LImages of LanguageW3 credits1V + 1UJ. L. Gastaldi
AbstractStudents 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.
ObjectiveBy 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.
ContentStudents 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.
851-0467-00LFrom Traffic Modeling to Smart Cities and Digital Democracies Information Restricted registration - show details
Number of participants limited to 30.
W3 credits2SD. Helbing, S. Mahajan
AbstractThis 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.
ObjectiveTo 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.
ContentThis 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.
LiteratureMartin 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
Link

Dirk Helbing
An Analytical Theory of Traffic Flow (collection of papers)
Link

Michael Batty, Kay Axhausen et al.
Smart cities of the future

Books by Michael Batty
Link

How social influence can undermine the wisdom of crowd effect
Link

Evidence for a collective intelligence factor in the performance of human groups
Link

Optimal incentives for collective intelligence
Link

Collective Intelligence: Creating a Prosperous World at Peace
Link

Big Mind: How Collective Intelligence Can Change Our World
Link

Programming Collective Intelligence
Link

Urban architecture as connective-collective intelligence. Which spaces of interaction?
Link

Build digital democracy
Link

How to make democracy work in the digital age
Link

Digital Democracy: How to make it work?
Link

Proof of witness presence: Blockchain consensus for augmented democracy in smart cities
Link

Iterative Learning Control for Multi-agent Systems Coordination
Link

Decentralized Collective Learning for Self-managed Sharing Economies
Link

Further literature will be recommended in the lectures.
851-0172-00LAround 1936: The New Language of Science Restricted registration - show details
Does not take place this semester.
Number of participants limited to 35.
W3 credits2S
AbstractThe 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.
ObjectiveDuring 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
ContentThe 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.
851-0098-00LWho and What is Reasonable. On Reason, AI and the Role of Science in SocietyW3 credits2GL. Wingert
AbstractTechnological developments and political conflicts provoke the question: Who and what is reasonable? Are robots reasonab or merely reliable? Is „reason = intelligence“ true? Are experts, e.g. climatologists, more reasonable than the folk? Should they have more political influence? Answering such questions requires a philosophical clarification of the concepts rationality, reason, and intelligence.
ObjectiveThe participant should have achieved
1. a knowledge of conceptions of reason and intelligence and of the difference between reason and intelligence;
2. an understanding of the ways robots and animals could be intelligent;
3. an evaluation of the role scientific experts should play in a democratic society.
851-0760-00LBuilding a Robot Judge: Data Science for Decision-Making Restricted registration - show details
Particularly suitable for students of D-INFK, D-ITET, D-MTEC
W3 credits2VE. Ash
AbstractThis 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.
ObjectiveThis 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.
ContentData 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.
851-0761-00LBuilding 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.
W2 credits2VE. Ash
AbstractStudents 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.
ObjectiveIn 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.
ContentStudents 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.
851-0125-65LA Sampler of Histories and Philosophies of Mathematics
Particularly suitable for students D-CHAB, D-INFK, D-ITET, D-MATH, D-PHYS
W3 credits2VR. Wagner
AbstractThis 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.
ObjectiveThe 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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