Search result: Catalogue data in Spring Semester 2021

GESS Science in Perspective Information
Only the courses listed below will be recognized as "GESS Science in Perspective" courses.

Further below you will find courses under the category "Type B courses Reflections about subject specific methods and content" as well as the language courses.

During the Bachelor’s degree Students should acquire at least 6 ECTS and during the Master’s degree 2 ECTS.

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 bachelor students after their first-year examination and for all master- or doctoral students.

Students who already took a course within their main study program are NOT allowed to take the same course again.

All these courses are listed under the category “Typ A”, this means, every student can enroll in these courses.
D-INFK
NumberTitleTypeECTSHoursLecturers
851-0585-38LData Science in Techno-Socio-Economic Systems Restricted registration - show details
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
W3 credits2VD. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite
AbstractThis course introduces how techno-socio-economic systems in our complex society can be better understood with techniques and tools of data science. Students shall learn how the fundamentals of data science are used to give insights into the research of complexity science, computational social science, economics, finance, and others.
ObjectiveThe goal of this course is to qualify students with knowledge on data science to better understand techno-socio-economic systems in our complex 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 tools, methods and techniques of data science.
In particular, lectures will be divided into research talks and tutorials. The course shall increase the awareness level of students of the importance of interdisciplinary research. Finally, students have the opportunity to develop their own data science skills based on a data challenge task, they have to solve, deliver and present at the end of the course.
ContentWill be provided on a separate course webpage.
Lecture notesSlides will be provided.
LiteratureGrus, Joel. "Data Science from Scratch: First Principles with Python". O'Reilly Media, 2019.
Link

"A high-bias, low-variance introduction to machine learning for physicists"
Link

Applications to Techno-Socio-Economic Systems:

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

"A network framework of cultural history"
Link

"Science of science"
Link

"Generalized network dismantling"
Link

Further literature will be recommended in the lectures.
Prerequisites / NoticeGood programming skills and a good understanding of probability & statistics and calculus are expected.
851-0740-00LBig Data, Law, and Policy Restricted registration - show details
Number of participants limited to 35.
Students will be informed by 1.3.2021 the latest.
W3 credits2SS. Bechtold
AbstractThis 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.
ObjectiveThis 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.
851-0732-03LIntellectual Property: An Introduction Information Restricted registration - show details
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.
W2 credits2VS. Bechtold, R. Zingg
AbstractThe 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.
ObjectiveIntellectual 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.
851-0727-01LTelecommunications Law
Particularly suitable for students of D-INFK, D-ITET
W2 credits2VC. von Zedtwitz
AbstractIntroduction 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).
ObjectiveBy 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.
Content1. 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 notesThe 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.
LiteratureNo mandatory readings.
Prerequisites / NoticeShort written exam at the end of the semester (scope and materials to be defined during the course).
851-0739-01LSequencing Legal DNA: NLP for Law and Political Economy
Particularly suitable for students of D-INFK, D-ITET, D-MTEC
W3 credits2VE. Ash
AbstractThis course explores the application of natural language processing techniques to texts in law, politics, and the news media.
ObjectiveStudents will be introduced to a broad array of tools in natural language processing (NLP). They will learn to evaluate and apply NLP tools to a variety of problems. The applications will focus on social-science contexts, including law, politics, and the news media. Topics include text classification, topic modeling, transformers, model explanation, and bias in language.
ContentNLP technologies have the potential to assist judges and other decision-makers by making tasks more efficient and consistent. On the other hand, language choices could be biased toward some groups, and automated systems could entrench those biases.

We will explore the use of NLP for social science research, not just in the law but also in politics, the economy, and culture. We will explore, critique, and integrate the emerging set of tools for debiasing language models and think carefully about how notions of fairness should be applied in this domain.
Prerequisites / NoticeSome programming experience in Python is required, and some experience with NLP is highly recommended.
851-0739-02LSequencing Legal DNA: NLP for Law and Political Economy (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
AbstractThis is the companion course for extra credit for a course project, for the course "Sequencing Legal DNA: NLP for Law and Political Economy".
ObjectiveStudents will be introduced to a broad array of tools in natural language processing (NLP). They will learn to evaluate and apply NLP tools to a variety of problems. The applications will focus on social-science contexts, including law, politics, and the news media. Topics include text classification, topic modeling, transformers, model explanation, and bias in language.
851-0165-00LQuestions Concerning the Philosophy of Mathematics, Theoretical Physics and Computer Science Restricted registration - show details W3 credits2SG. Sommaruga, S. Wolf
AbstractThis seminar tackles questions of the philosophy of mathematics, of theoretical physics ad computer science which are rather non-standard such as: Are proofs really constitutive of mathematics? Why are applications of mathematics (to nature but also to mathematics itself) so fascinating and so hard to understand? etc.
ObjectiveThe objective is not so much to get acquainted with basic concepts and theories in the philosophy of mathematics, of theoretical physics and computer science, but to reflect in a methodical way about what lies at the origin of these philosophies. Students should learn to articulate questions arising during their studies and to pursue them in a more systematic way.
ContentThis seminar tackles questions of the philosophy of mathematics, of theoretical physics ad computer science which are rather non-standard such as: Are proofs really constitutive of mathematics? Why are applications of mathematics (to nature but also to mathematics itself) so fascinating and so hard to understand? Why do certain physical theories, e.g. quantum mechanics, need an "interpretation" whereas others don't? Is computer science part of discrete mathematics or a natural science? etc.
851-0173-00LHistory of Formal Logic: The Emergence of Boolean Logic Restricted registration - show details W3 credits2VJ. L. Gastaldi
AbstractThe invention of Boolean logic in the middle of the 19th century is considered a major event in the history of modern thought. However, Boole’s original system does not correspond to what we came to understand as Boolean logic.
We will study the early history of Boolean logic in relation to the mathematics of its epoch, in search of an alternative philosophy of formal knowledge for the present.
ObjectiveDuring the course, students will be able to:
-Acquire a general perspective on the history of formal logic
-Review relevant aspects of the history of modern mathematics
-Obtain philosophical and historical tools for critically assessing the status of formal sciences
-Develop a critical understanding of the notion of formal
-Discuss the methodological capabilities of historical epistemology
ContentThe invention of Boolean logic in the middle of the 19th century is considered a major event in the history of modern thought. Boolean algebras and Boolean rings lay at the basis of propositional logic and digital communication, contributing in a decisive way to the theoretical and technical conditions of our time. However, if attention is paid to Boole’s own work, it will quickly appear that his Calculus of Logic does not correspond to what we came to understand as Boolean logic. Instead of disregarding those differences as inevitable mistakes of any pioneering enterprise, waiting to be corrected by successive developments, we will try to understand them as the sign of an alternative philosophy of logic and formal knowledge, which later developments excluded and forgot, and from which recent advances in formal sciences could take advantage. Such an inquiry will give us the occasion of exploring the philosophical and scientific landscape in which formal logic emerged in the first half of the 19th century (focusing on the works of Babbage, De Morgan and Boole), and to build a critical perspective on the notion of “formal”, at the crossroad of the history and philosophy of mathematics and logic.
851-0174-00LRebooting AI: Human and Social Aspects of Artificial Intelligence Restricted registration - show details
Suitable only for MA and PhD students
W3 credits2GJ. L. Gastaldi, O. Del Fabbro, A. Nardo, D. Trninic
AbstractSeveral researchers from the humanities will propose a critical yet not partisan approach to AI, aiming at elaborating a common perspective on this phenomenon. Sessions will delve into aspects of the way in which AI challenges our understanding of the human, such as “Knowledge”, “Learning”, “Language”, “Freedom” or “Justice”.
ObjectiveDuring the course, students will be able to:
-Discuss relevant aspects of the impact of AI in human and social life
-Obtain theoretical and methodological tools for critically assessing the place of technology in society
-Develop a critical understanding of the conceptual grounds of AI
-Acquire a general perspective on the different fields and points of views in the humanities
-Engage in collaborative work with researchers in the humanities
ContentThe last decades have witnessed a remarkable development in the field of Artificial Intelligence (AI). Although mainly technical feat, such advances have decisive consequences in a wide variety of aspects of human and social life. Even more, AI is challenging in multiple ways our very understanding of what is to be a human. However, despite the significance of the transformations at stake, the perspectives of the humanities -traditionally established as a valid source of critical inquiry into human matters- are generally relegated to a secondary role in the development of AI.

In this seminar, several researchers from the humanities will propose a critical yet not partisan approach to AI, aiming at elaborating a common perspective which could be taken as a legitimate interlocutor in the debates arising around the current stakes of technology in our society. The seminar will take the form of presentations based on critical readings of chosen texts, followed by group discussions. Each session will delve into one aspect of the way in which AI challenges our understanding of the human, such as “Knowledge”, “Learning”, “Language”, “Freedom” or “Justice”, confronting how they are dealt with in state-of-the-art texts in AI and relevant works in the humanities.

We expect students from science, technology, engineering, and mathematics and other fields outside the humanities to actively contribute to a collective construction, which could lead to further collaboration within but also outside this course.

As part of the Turing Centre, this seminar intends to sow the seed of a suitable and long-term environment for the exchange of ideas between multiple fields in the natural sciences and the humanities.

The seminar will be conducted by Olivier Del Frabbro, Juan Luis Gastaldi, Aline Nardo, Vanessa Rampton and Dragan Trninic.
Prerequisites / NoticeSuitable only for MA and PhD students
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