Search result: Catalogue data in Spring Semester 2022
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| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 402-0812-00L | Computational Statistical Physics | W | 8 credits | 2V + 2U | M. Krstic Marinkovic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The lecture will give a deeper insight into computer simulation methods in statistical physics. Thus, it is an ideal continuation of the lecture "Introduction to Computational Physics" of the autumn semester. In the first part students learn to apply the following methods: Classical Monte Carlo-simulations, finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Moreover, students learn about the application of statistical physics methods to Boltzmann machines and how to simulate non-equilibrium systems. In the second part, students apply molecular dynamics simulation methods. This part includes long range interactions, Ewald summation and discrete elements. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes and slides are available online and will be distributed if desired. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature recommendations and references are included in the lecture notes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Some basic knowledge about statistical physics, classical mechanics and computational methods is recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 402-1782-00L | Physics II | W | 7 credits | 4V + 2U | R. Wallny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to theory of waves, electricity and magnetism. This is the continuation of Physics I which introduced the fundamentals of mechanics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | basic knowledge of mechanics and electricity and magnetism as well as the capability to solve physics problems related to these subjects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-0702-00L | Statistical Models in Computational Biology | W | 6 credits | 2V + 1U + 2A | N. Beerenwinkel | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Graphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252 - Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007. - Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 851-0370-00L | Didactic Basics for Student Teaching Assistants | W | 1 credit | 1S | S. Pedrocchi, M. Lehner, B. Volk | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course “Didactic Basics for Student Teaching Assistants” enhance Student Teaching Assistants (Student TAs) to develop knowledge, capability and confidence to effectively plan and teach courses and exercises. Participants get trained to think critically about students’ learning and create learning situations in which students are actively engaged. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | In this course Student Teaching Assistants will ... • reflect on their approach to teaching as well as their attitude towards teaching. • understand the basics of teaching and learning in the context of their subject. • consciously design the introduction of their course as well as the introduction of single teaching units. • apply classroom assessment techniques as formative assessments to measure the current status of their students. • develop a didactic concept according to the learning objectives. • conduct interactive sequences as learning activities. • give and get feedback from peers and self-reflect on their teaching practice. • feel confident to use methods for active learning scenarios in their classes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The online phase with 4 chapters will provide a range of relevant topics for developing the teaching competence of Student Teaching Assistants: • Chapter 1 presents an overview about how learning works. Based on these fundamentals of learning participants reflect on their role as Student TAs to feel comfortable in their new role as a teacher. • In chapter 2 Student TAs start planning an own lesson by introducing a class and locate it in the larger topic (methods: portal and informative introduction). • In chapter 3 Student TAs learn to plan learning activities in order to activate students (active learning methods). • Chapter 4 is about giving and also getting feedback. The participants integrate this topic also in their lesson plan. While working through the four chapters, Student TAs have the chance to reflect, exchange ideas with peers and plan their own teaching accordingly so that they feel confident in their role. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Self-paced online course: https://moodle-app2.let.ethz.ch/course/view.php?id=16327 Consolidation Workshops will take place in April 2022. The dates will be announced in the online course at the beginning of the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 851-0557-00L | Soccer Analytics Students should be comfortable with mathematical derivations and scripting for data analysis. | W | 3 credits | 2G | U. Brandes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Soccer analytics refers to the use of data in tactical decision-making, strategic planning, and fan engagement in the context of association football. This course is first and foremost about data, problems, and methods. They are discussed, however, with reference to the broader context of measurement and data science in sports and society. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain insight into the role of data science in professional football. They learn about attempts to capture aspects of the beautiful game in observable data to inform tactical, strategic, and communicative decision-making. By appreciating difficulties that arise even in activities with highly regulated interactions such as team sports, they reflect on the use of data science in the study of collective behavior. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The content is organized into lectures with time for reflective discussions and a practical part, in which small teams use free software tools to gain first-hand experience in working with sports data. The following is a tentative overview of course contents, with exemplary aspects listed for each topic. A major element for each of the analytic topics are various forms of visualization such as timelines, step plots, scatterplots, density maps, shot maps, and networks. 1. Introduction - history of measurement and analytics in sports - laws of the game: equipment, space, time, players - data: master, match, event, tracking; sources, availability, uses 2. Scores - competitions: tournaments, leagues - ranking teams: coefficients, latent strengths - predicting results: odds, statistics 3. Individual Actions - running: heatmaps, pitch control - passing: packing, line breaking, crosses - shooting: expected goals & co. 4. Match Phases - set pieces, penalties, free kicks, etc. - possession, location, organization 5. Collective Behavior - formations: spatial distributions, proximity networks - attacking: possession value, positional play, passing networks - defending: (counter-)pressure, marking networks - team composition: plus/minus, interactions 6. Environment - recruitment: player profiles, transfer market, agents, salaries - governance: clubs, leagues, associations, confederations - engagement: attendance, merchandise, social media - simulation: robocup, esports, fantasy football - betting market Fair warning: This is the first edition of the course and it may be adjusted depending on interest and feedback. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Credits are awarded for active participation and a group project. To get the most out of the project, basic knowledge of programming languages such as python or R is advisable. Whether the course is offered again will be decided at the end of the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 851-0585-38L | Data Science in Techno-Socio-Economic Systems  Number of participants limited to 130. 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 | D. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Will be provided on a separate course webpage. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Grus, Joel. "Data Science from Scratch: First Principles with Python". O'Reilly Media, 2019. https://dl.acm.org/doi/10.5555/2904392 "A high-bias, low-variance introduction to machine learning for physicists" https://www.sciencedirect.com/science/article/pii/S0370157319300766 Applications to Techno-Socio-Economic Systems: "The hidden geometry of complex, network-driven contagion phenomena" (relevant for modeling pandemic spread) https://science.sciencemag.org/content/342/6164/1337 "A network framework of cultural history" https://science.sciencemag.org/content/345/6196/558 "Science of science" https://science.sciencemag.org/content/359/6379/eaao0185.abstract "Generalized network dismantling" https://www.pnas.org/content/116/14/6554 Further literature will be recommended in the lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Good programming skills and a good understanding of probability & statistics and calculus are expected. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 851-0602-00L | Shaping a DCent.Society: Assessing Societal Implications of Bitcoin, Blockchains & Smart Contracts | W | 3 credits | 2V | M. M. Dapp | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course investigates the potential long-term implications of distributed ledger technology on our societies. Students critically reflect the economic, political, ecological, and ethical implications of the Bitcoin cryptocurrency and the Ethereum smart contract engine (incl. DeFi) by exploring connections to disciplines such as economics, political science, psychology, sociology, and philosophy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Compare the paradigm shift from Web 2.0 to Web 3.0 Distinguish a broad range of Web 3.0 concepts Hypothesize about economic, political, ecological, and ethical implications of Bitcoin, Ethereum, and decentralized applications Integrate ethical and governance considerations into the design of cryptoeconomic systems Justify own opinions about societal implications of decentralizing society | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Imagine... what if Bitcoin, Ethereum, and related distributed ledger technology will be wildly successful and flourish long-term? Which parts of our economies and societies would they affect? Could we indeed redesign our societies towards more sustainable action, more democratic governance, and more equitable finance by envisioning new ways of organizing, coordinating, and acting collectively? Or is this all make-belief because, after all, the Internet also under-delivered in important aspects of its huge promises? How can we critically reflect on the long-term implications of decentralizing technologies on our societies? Bitcoin is dividing the world. Due to its erratic price movements, some view Bitcoin as a useless Ponzi scheme at best and a complex, state-interfering “thing” at worst. Others, however herald it as the most important invention since the Internet or the printing press. In any case, the questions raised by Bitcoin are not only of academic interest: Is today’s fiat money system fair? Should people or the state create money? Is global anonymous transfer of digital value a good thing or not? Will Bitcoin supercharge renewable energy or do we need to switch it off to save the planet? Could it even bring peace by preventing states from financing wars or is this a preposterous claim? Ethereum, blockchain technology, smart contracts, and decentralized applications (dApps) seem to be less contentious and have caught the interest of companies and government for their specific technical characteristics. However, where is the evidence that decentralized technology is beneficial inside a hierarchical, “trusted” setting? Will unstoppable dApps empower us or create rigid machines steering our behavior? So, what to make of this extremely polarized debate and how to come to reasonable own conclusions when imagining the decentralization of society? The course aims to connect the cultural and historical preconditions to the long-term societal implications of Bitcoin, Ethereum, blockchains, smart contracts, and dApps. We will research and critically reflect economic, political, ecological and ethical consequences with the aim to formulate our own opinions about what is currently happening and what might happen in the future. To achieve this multi-disciplinary goal, we establish a common understanding of the technologies and inner workings of Bitcoin, Ethereum & Co. in the first part. We discuss selected aspects such as open source software, cryptography, cryptoeconomics, incentives, and complex systems. Why and how is Bitcoin a “trustless” system – or is it not? Why is an absolute scarce digital asset a big deal – or is it not? Why and how is Ethereum a “world computer” – or is it not? Why is an unstoppable system of dApps and decentralized autonomous organizations (DAOs) a big deal – or is it not? For a full picture, we will also examine other developments such as altcoins, Decentralized Finance (DeFi), stablecoins, and Central Bank Digital Currencies. This introduction will provide the technical background to move to the main part of the course, in which we go into depth on the potential societal implications of Bitcoin, Ethereum & Co. We will be covering various domains such as sound and fair money & its value, free trade & prosperity, incentive design & social behavior, sustainability & energy use, individual sovereignty & state control, democracy & geopolitics. We will thus be exploring connections between information technology and economics, political science, psychology, sociology, and philosophy. Throughout the course, students are regularly invited to debate in small interventions. They will work in teams to build their own critical analysis and arguments about a specific challenge/issue chosen from the course material. They will summarize their conclusions in a brief report and defend them in class in the final part of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides will be distributed on a weekly basis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Ammous, Saifedean. The Bitcoin Standard: The Decentralized Alternative to Central Banking. Hoboken, New Jersey: Wiley, 2018. Antonopoulos, Andreas M. Mastering Bitcoin: Programming the Open Blockchain. 2nd ed. O’Reilly, 2017. Antonopoulos, Andreas M., and Gavin Wood. Mastering Ethereum: Building Smart Contracts and Dapps. O’reilly Media, 2018. Dapp, Marcus M., Dirk Helbing, and Stefan Klauser, eds. Finance 4.0 - Towards a Socio-Ecological Finance System: A Participatory Framework to Promote Sustainability. SpringerBriefs in Applied Sciences and Technology. Cham: Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-71400-0. Dapp, Marcus M. “Toward a Sustainable Circular Economy Powered by Community-Based Incentive Systems.” In Business Transformation Through Blockchain, edited by Horst Treiblmaier and Roman Beck. Springer, 2019. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | For this ambitious and interactive course, we hope to attract students who are motivated by tackling large societal challenges with new decentralized approaches to human coordination. We think students with an open mind and interest in interdisciplinary aspects of their field of study will benefit most from this course. Programming experience is not strictly required but some basics about computer science may be helpful to see the potential societal implications of this new technology paradigm. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 851-0739-01L | Natural Language Processing for Law and Social Science Particularly suitable for students of D-INFK, D-ITET, D-MTEC | W | 3 credits | 2V | E. Ash | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course explores the application of natural language processing techniques to texts in law, politics, and the news media. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | NLP 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 / Notice | Some programming experience in Python is required, and some experience with NLP is highly recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 851-0739-02L | Natural Language Processing for Law and Social Science (Course Project) This is the optional course project for "Natural Language Processing for Law and Social Science". 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 | This is the companion course for extra credit for a course project, for the course "Natural Language Processing for Law and Social Science". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Science in Perspective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Science in Perspective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| » see Science in Perspective: Type A: Enhancement of Reflection Capability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| » Recommended Science in Perspective (Type B) for D-INFK | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Language Courses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| » see Science in Perspective: Language Courses ETH/UZH | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Bachelor's Thesis | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0500-00L | Bachelor's Thesis | O | 10 credits | 21D | Professors | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The Bachelor Programme concludes with the Bachelor Thesis. This project is supervised by a professor. Writing up the Bachelor Thesis encourages students to show independence and to produce structured work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Encourages students to show independence, to produce scientifically structured work and to apply engineering working methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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