Search result: Catalogue data in Spring Semester 2022
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| 252-2603-00L | Seminar on Systems Security  Number of participants limited to 22. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | S. Shinde | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The seminar focuses on critical thinking and critique of fundamental as well as recent advances in systems security. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The learning objective is to analyze selected research papers published at top systems+security venues and then identify open problems in this space. The seminar will achieve this via several components: reading papers, technical presentations, writing analysis and critique summaries, class discussions, and exploring potential research topics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Each student will pick one paper from the selected list, present it in the class, and lead the discussion for that paper. During the semester, all students will select, read, and submit critique summaries for at least 8 research papers from the list. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students who are either interested in security research or are exploring thesis topics are highly encouraged to take this course. Students with systems/architecture/verification/PL expertise and basic security understanding are welcome. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 252-4102-00L | Seminar on Randomized Algorithms and Probabilistic Methods The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. Number of participants limited to 24. | W | 2 credits | 2S | A. Steger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The aim of the seminar is to study papers which bring the students to the forefront of today's research topics. This semester we will study selected papers of the conference Symposium on Discrete Algorithms (SODA22). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Read papers from the forefront of today's research; learn how to give a scientific talk. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The seminar is open for both students from mathematics and students from computer science. As prerequisite we require that you passed the course Randomized Algorithms and Probabilistic Methods (or equivalent, if you come from abroad). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-5704-00L | Advanced Methods in Computer Graphics Number of participants limited to 24. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | M. Gross, O. Sorkine Hornung | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar covers advanced topics in computer graphics with a focus on the latest research results. Topics include modeling, rendering, visualization, animation, physical simulation, computational photography, and others. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal is to obtain an in-depth understanding of actual problems and research topics in the field of computer graphics as well as improve presentation and critical analysis skills. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 261-5113-00L | Computational Challenges in Medical Genomics Number of participants limited to 20. | W | 2 credits | 2S | A. Kahles | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar discusses recent relevant contributions to the fields of computational genomics, algorithmic bioinformatics, statistical genetics and related areas. Each participant will hold a presentation and lead the subsequent discussion. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Preparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to succesfully present the key points of existing research work are the same as needed to communicate own research ideas. In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The topics covered in the seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc. Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are amongst the list of selected papers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Knowledge of algorithms and data structures and interest in applications in genomics and computational biomedicine. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 263-2100-00L | Research Topics in Software Engineering Number of participants limited to 22. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | Z. Su, M. Vechev | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar is an opportunity to become familiar with current research in software engineering and more generally with the methods and challenges of scientific research. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Each student will be asked to study some papers from the recent software engineering literature and review them. This is an exercise in critical review and analysis. Active participation is required (a presentation of a paper as well as participation in discussions). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The aim of this seminar is to introduce students to recent research results in the area of programming languages and software engineering. To accomplish that, students will study and present research papers in the area as well as participate in paper discussions. The papers will span topics in both theory and practice, including papers on program verification, program analysis, testing, programming language design, and development tools. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The publications to be presented will be announced on the seminar home page at least one week before the first session. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Papers will be distributed during the first lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-2926-00L | Deep Learning for Big Code Number of participants limited to 24. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | V. Raychev | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The seminar covers some of the latest and most exciting developments (industrial and research) in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of the seminar is to: - Introduce students to the field of Deep Learning for Big Code. - Learn how machine learning models can be used to solve practical challenges in software engineering and programming beyond traditional methods. - Highlight the latest research and work opportunities in industry and academia available on this topic. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The last 5 years have seen increased interest in applying advanced machine learning techniques such as deep learning to new kind of data: program code. As the size of open source code increases dramatically (over 980 billion lines of code written by humans), so comes the opportunity for new kind of deep probabilistic methods and commercial systems that leverage this data to revolutionize software creation and address hard problems not previously possible. Examples include: machines writing code, program de-obfuscation for security, code search, and many more. Interestingly, this new type of data, unlike natural language and images, introduces technical challenges not typically encountered when working with standard datasets (e.g., images, videos, natural language), for instance, finding the right representation over which deep learning operates. This in turn has the potential to drive new kinds of machine learning models with broad applicability. Because of this, there has been substantial interest over the last few years in both industry (e.g., companies such as Facebook starting, various start-ups in the space such as http://deepcode.ai), academia (e.g., http://plml.ethz.ch) and government agencies (e.g., DARPA) on using machine learning to automate various programming tasks. In this seminar, we will cover some of the latest and most exciting developments in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities. The seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation. The seminar is ideally suited for M.Sc. students in Computer Science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-3600-00L | Heterogeneous Systems Seminar Number of participants limited to 36. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | M. J. Giardino | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The seminar covers heterogeneous systems, those that make use of different types of computing (GPUs, FPGA, ASICs, etc.) and/or memory (NVM/SCM). Our focus will be the systems and architectures that use these devices. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of this course is to familiarize students with important topics in heterogeneous systems, past, present, and future: the devices, the architectures, and their uses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar consists of student presentations based upon a list of papers provided at the beginning of the course. Presentations will be done in teams. Students will be allotted a 45 minute time slot consisting of a 30 minute presentation and 15 minutes for questions. Grading is based upon the quality of the presentation, the coverage of the paper including necessary background and follow-on work, and the ability to understand and critique the paper and technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-3712-00L | Advanced Seminar on Computational Haptics Number of participants limited to 14. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | O. Hilliges | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Haptic rendering technologies stimulate the user’s senses of touch and motion just as felt when interacting with physical objects. Actuation techniques need to address three questions: 1) What to actuate, 2) How to actuate it and 3) When to actuate it. We will approach each of these questions from a heavily technical perspective, with a focus on optimization and machine learning to find answers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of the seminar is to familiarize students with exciting new research topics in this important area, but also to teach basic scientific writing and oral presentation skills. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Haptics rendering is the use of technology that stimulates the senses of touch and motion that would be felt by a user interacting directly with physical objects. This usually involves hardware that is capable of delivering these senses. Three questions arise here: 1) What to actuate, 2) How to actuate it and 3) When to actuate. We will approach these questions from a heavy technical perspective that usually have an optimization or machine learning focus. Papers from scientific venues such as CHI, UIST & SIGGRAPH will be examined in-depth that answer these questions (partially). Students present and discuss the papers to extract techniques and insights that can be applied to software & hardware projects. Topics revolve around computational design, sensor placement, user state interference (through machine learning), and actuation as an optimization problem. The seminar will have a different structure from regular seminars to encourage more discussion and a deeper learning experience. We will use a case-study format where all students read the same paper each week but fulfill different roles and hence prepare with different viewpoints in mind ( "presenter", "historian", "PhD", and “Journalist”). The final deliverables include: 20 Minute presentation as presenter 5 Minute presentation as historian 1 A4 research proposal as the PhD 1 A4 summary of the discussion as the Journalist. Example papers are: Tactile Rendering Based on Skin Stress Optimization - (http://mslab.es/projects/TactileRenderingSkinStress/) SIGGRAPH 2020 SimuLearn: Fast and Accurate Simulator to Support Morphing Materials Design and Workflows - (https://dl.acm.org/doi/10.1145/3379337.3415867) UIST 2019 Fabrication-in-the-Loop Co-Optimization of Surfaces and Styli for Drawing Haptics -(https://www.pdf.inf.usi.ch/projects/SurfaceStylusCoOpt/index.html) SIGGRAPH 2020 For each topic, a paper will be chosen that represents the state of the art of research or seminal work that inspired and fostered future work. Students will learn how to incorporate computational methods into systems that involve software, hardware, and, very importantly, users. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Computational Interaction, Edited by Antti Oulasvirta, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes, 2018. PDF Freely available through the ETH Network. Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-4203-00L | Geometry: Combinatorics and Algorithms The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | B. Gärtner, M. Hoffmann, E. Welzl, J. Cardinal, M. Wettstein | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Each student is expected to read, understand, and elaborate on a selected research paper. To this end, (s)he should give a 45-min. presentation about the paper. The process includes * getting an overview of the related literature; * understanding and working out the background/motivation: why and where are the questions addressed relevant? * understanding the contents of the paper in all details; * selecting parts suitable for the presentation; * presenting the selected parts in such a way that an audience with some basic background in geometry and graph theory can easily understand and appreciate it. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This seminar is held once a year and complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent. The seminar is a good preparation for a master, diploma, or semester thesis in the area. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisite: Successful participation in the course "Geometry: Combinatorics & Algorithms" (takes place every HS) is required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-4651-00L | Current Topics in Cryptography Number of participants limited to 24. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | D. Hofheinz, U. Maurer, K. Paterson | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this seminar course, students present and discuss a variety of recent research papers in Cryptography. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Independent study of scientific literature and assessment of its contributions as well as learning and practicing presentation techniques. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course lecturers will provide a list of papers from which students will select. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The reading list will be published on the course website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Ideally, students will have taken the D-INFK Bachelors course “Information Security" or an equivalent course at Bachelors level. Ideally, they will have attended or will attend in parallel the Masters course in "Applied Cryptography”. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-5225-00L | Advanced Topics in Machine Learning and Data Science Number of participants limited to 20. The deadline for deregistering expires at the end of the fourth week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | F. Perez Cruz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this seminar, recent papers of the machine learning and data science literature are presented and discussed. Possible topics cover statistical models, machine learning algorithms and its applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The seminar “Advanced Topics in Machine Learning and Data Science” familiarizes students with recent developments in machine learning and data science. Recently published articles, as well as influential papers, have to be presented and critically reviewed. The students will learn how to structure a scientific presentation, which covers the motivation, key ideas and main results of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth for the audience to be able to follow its main conclusion, especially why the article is (or is not) worth attention. The presentation style will play an important role and should reach the level of professional scientific presentations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar will cover a number of recent papers which have emerged as important contributions to the machine learning and data science literatures. The topics will vary from year to year but they are centered on methodological issues in machine learning and its application, not only to text or images, but other scientific domains like medicine, climate or physics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The papers will be presented in the first session of the seminar. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-5904-00L | Deep Learning for Computer Vision: Seminal Work Number of participants limited to 24. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | I. Armeni | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar covers seminal papers on the topic of deep learning for computer vision. The students will present and discuss the papers and gain an understanding of the most influential research in this area - both past and present. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objectives of this seminar are two-fold. Firstly, the aim is to provide a solid understanding of key contributions to the field of deep learning for vision (including a historical perspective as well as recent work). Secondly, the students will learn to critically read and analyse original research papers and judge their impact, as well as how to give a scientific presentation and lead a discussion on their topic. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar will start with introductory lectures to provide (1) a compact overview of challenges and relevant machine learning and deep learning research, and (2) a tutorial on critical analysis and presentation of research papers. Each student then chooses one paper from the provided collection to present during the remainder of the seminar. The students will be supported in the preparation of their presentation by the seminar assistants. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The selection of research papers will be presented at the beginning of the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The course "Machine Learning" is recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0126-00L | Advanced Topics in Networked Embedded Systems | W | 2 credits | 1S | L. Thiele | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The seminar will cover advanced topics in networked embedded systems. A particular focus are cyber-physical systems, internet of things, and sensor networks in various application domains. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal is to get a deeper understanding on leading edge technologies in the discipline, on classes of applications, and on current as well as future research directions. In addition, participants will improve their presentation, reading and reviewing skills. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar enables Master students, PhDs and Postdocs to learn about latest breakthroughs in wireless sensor networks, networked embedded systems and devices, and energy-harvesting in several application domains, including environmental monitoring, tracking, smart buildings and control. Participants are requested to actively participate in the organization and preparation of the seminar. In particular, they review all presented papers using a standard scientific reviewing system, they present one of the papers orally and they lead the corresponding discussion session. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0559-00L | Seminar in Deep Neural Networks Number of participants limited to 25. | W | 2 credits | 2S | R. Wattenhofer, P. Belcák, B. Egressy | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this seminar participating students present and discuss recent research papers in the area of deep neural networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | We aim at giving the students an in depth view on the current advances in the area by discussing recent papers as well as discussing current issues and difficulties surrounding deep neural networks. The students will learn to read, evaluate and challenge research papers, prepare coherent scientific presentations and lead a discussion on their topic. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar will cover a range of research directions, with a focus on Graph Neural Networks, Algorithmic Learning, Reinforcement Learning and Natural Language Processing. It will be structured in blocks with each focus area being briefly introduced before presenting and discussing recent research papers. Papers will be allocated to the students based on their preferences. For more information see www.disco.ethz.ch/courses.html. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides of presentations will be made available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The paper selection can be found on www.disco.ethz.ch/courses.html. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | It is expected that students have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0559-10L | Seminar in Communication Networks Does not take place this semester. Number of participants limited to 12. | W | 2 credits | 2S | L. Vanbever | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this seminar, students review, present, and discuss recent research papers in the area of computer networks. The seminar also includes a small experimental group project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | By the end of the seminar, students will be able to 1. Read efficiently and assess critically scientific papers; 2. Discuss technical topics with an audience of peers; 3. Design and conduct simple networking experiments. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar will start with one introductory lecture. Starting from the second week, participating students will start reviewing, presenting, and discussing research papers. Two papers will be discussed each week. Each student must choose a paper from a given list, prepare and give a (short) presentation on the paper's topic, and lead the follow-up discussion. In addition, all students submit one (short) review for the two papers presented every week in-class. During the last weeks of the seminar, students will work on a small group project, which consists in trying to replicate one experiment (freely chosen) from the research papers discussed in the seminar. Students will be evaluated based on their reviews, their presentation, their leadership of and participation in the paper discussions, as well as their group project. The exact course content varies over time. For details, refers to the course website: https://seminar-net.ethz.ch/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The slides of each presentation will be made available on the website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The paper selection will be made available on the course website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Communication Networks (227-0120-00L) or equivalents. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 851-0740-00L | Big Data, Law, and Policy Does not take place this semester. Number of participants limited to 35. | W | 3 credits | 2S | S. Bechtold | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces students to societal perspectives on the big data revolution. Discussing important contributions from machine learning and data science, the course explores their legal, economic, ethical, and political implications in the past, present, and future. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course is intended both for students of machine learning and data science who want to reflect on the societal implications of their field, and for students from other disciplines who want to explore the societal impact of data sciences. The course will first discuss some of the methodological foundations of machine learning, followed by a discussion of research papers and real-world applications where big data and societal values may clash. Potential topics include the implications of big data for privacy, liability, insurance, health systems, voting, and democratic institutions, as well as the use of predictive algorithms for price discrimination and the criminal justice system. Guest speakers, weekly readings and reaction papers ensure a lively debate among participants from various backgrounds. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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