# Suchergebnis: Katalogdaten im Herbstsemester 2018

Data Science Master | ||||||

Interdisziplinäre Wahlfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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227-0575-00L | Advanced Topics in Communication Networks (Autumn 2018) | W | 6 KP | 2V + 2U | L. Vanbever | |

Kurzbeschreibung | This class will introduce students to advanced, research-level topics in the area of communication networks, both theoretically and practically. Coverage will vary from semester to semester. Repetition for credit is possible, upon consent of the instructor. During the Fall Semester of 2018, the class will concentrate on network programmability and network data plane programming. | |||||

Lernziel | The goal of this lecture is to introduce students to the latest advances in the area of computer networks, both theoretically and practically. The course will be divided in two main blocks. The first block (~7 weeks) will interleave classical lectures with practical exercises and paper readings. The second block (~6 weeks) will consist of a practical project involving real network hardware and which will be performed in small groups (~3 students). During the second block, lecture slots will be replaced by feedback sessions where students will be able to ask questions and get feedback about their project. The last week of the semester will be dedicated to student presentations and demonstrations. During the Fall Semester 2018, the class will focus on programmable network data planes and will involve developing network applications on top of the the latest generation of programmable network hardware: Barefoot Network’s Tofino switch ASICs. By leveraging data-plane programmability, these applications can build deep traffic insights to, for instance, detect traffic anomalies (e.g. using Machine Learning), flexibly adapt forwarding behaviors (to improve performance), speed-up distributed applications (e.g. Map Reduce), or track network-wide health. More importantly, all this can now be done at line-rate, at forwarding speeds that can reach Terabits per second. | |||||

Inhalt | Traditionally, computer networks have been composed of "closed" network devices (routers, switches, middleboxes) whose features, forwarding behaviors and configuration interfaces are exclusively defined on a per-vendor basis. Innovating in such networks is a slow-paced process (if at all possible): it often takes years for new features to make it to mainstream network equipments. Worse yet, managing the network is hard and prone to failures as operators have to painstakingly coordinate the behavior of heterogeneous network devices so that they, collectively, compute a compatible forwarding state. Actually, it has been shown that the majority of the network downtimes are caused by humans, not equipment failures. Network programmability and Software-Defined Networking (SDN) have recently emerged as a way to fundamentally change the way we build, innovate, and operate computer networks, both at the software *and* at the hardware level. Specifically, programmable networks now allow: (i) to adapt how traffic flows in the entire network through standardized software interfaces; and (ii) to reprogram the hardware pipeline of the network devices, i.e. the ASICs used to forward data packets. This year, the course will focus on reprogrammable network hardware/ASICs. It will involve hands-on experience on the world's fastest programmable switch to date (i.e. Barefoot Tofino switch ASIC). Among others, we'll cover the following topics: - The fundamentals and motivation behind network programmability; - The design and optimization of network control loops; - The use of advanced network data structures adapted for in-network execution; - The P4 programming language and associated runtime environment; - Hands-on examples of in-network applications solving hard problems in the area of data-centers, wide-area networks, and ISP networks. The course will be divided in two blocks of 7 weeks. The first block will consist in traditional lectures introducing the concepts along with practical exercises to get acquainted with programmable data planes. The second block will consist of a (mandatory) project to be done in groups of few students (~3 students). The project will involve developing a fully working network application and run it on top of real programmable network hardware. Students will be free to propose their own application or pick one from a list. At the end of the course, each group will present its application in front of the class. | |||||

Skript | Lecture notes and material will be made available before each course on the course website. | |||||

Literatur | Relevant references will be made available through the course website. | |||||

Voraussetzungen / Besonderes | Prerequisites: Communication Networks (227-0120-00L) or equivalents / good programming skills (in any language) are expected as both the exercices and the final project will involve coding. | |||||

263-3900-00L | Communication Networks Seminar Number of participants limited to 20. 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 KP | 2S | A. Singla | |

Kurzbeschreibung | We explore recent advances in networking by reading high quality research papers, and discussing open research opportunities, most of which are suitable for students to later take up as thesis or semester projects. | |||||

Lernziel | The objectives are (a) to understand the state-of-the-art in the field; (b) to learn to read, present and critique papers; (c) to engage in discussion and debate about research questions; and (d) to identify opportunities for new research. Students are expected to attend the entire seminar, choose a topic for presentation from a given list, make a presentation on that topic, and lead the discussion. Further, for each reading, every student needs to submit a review before the in-class discussion. Students are evaluated on their submitted reviews, their presentation and discussion leadership, and participation in seminar discussions. | |||||

Literatur | A program will be posted here: https://ndal.ethz.ch/courses/networks-seminar.html, comprising of a list of papers the seminar group will cover. | |||||

Voraussetzungen / Besonderes | An undergraduate-level understanding of networking, such that the student is familiar with concepts like reliable transport protocols (like TCP) and basics of Internet routing. ETH courses that fulfill this requirement: Computer Networks (252-0064-00L) and its predecessor (Operating Systems and Networks -- 252-0062-00L). Similar courses at other universities are also sufficient. | |||||

401-3922-00L | Life Insurance Mathematics | W | 4 KP | 2V | M. Koller | |

Kurzbeschreibung | The classical life insurance model is presented together with the important insurance types (insurance on one and two lives, term and endowment insurance and disability). Besides that the most important terms such as mathematical reserves are introduced and calculated. The profit and loss account and the balance sheet of a life insurance company is explained and illustrated. | |||||

Lernziel | ||||||

401-3925-00L | Non-Life Insurance: Mathematics and Statistics | W | 8 KP | 4V + 1U | M. V. Wüthrich | |

Kurzbeschreibung | The lecture aims at providing a basis in non-life insurance mathematics which forms a core subject of actuarial sciences. It discusses collective risk modeling, individual claim size modeling, approximations for compound distributions, ruin theory, premium calculation principles, tariffication with generalized linear models, credibility theory, claims reserving and solvency. | |||||

Lernziel | The student is familiar with the basics in non-life insurance mathematics and statistics. This includes the basic mathematical models for insurance liability modeling, pricing concepts, stochastic claims reserving models and ruin and solvency considerations. | |||||

Inhalt | The following topics are treated: Collective Risk Modeling Individual Claim Size Modeling Approximations for Compound Distributions Ruin Theory in Discrete Time Premium Calculation Principles Tariffication and Generalized Linear Models Bayesian Models and Credibility Theory Claims Reserving Solvency Considerations | |||||

Skript | M. V. Wüthrich, Non-Life Insurance: Mathematics & Statistics http://ssrn.com/abstract=2319328 | |||||

Voraussetzungen / Besonderes | The exams ONLY take place during the official ETH examination period. This course will be held in English and counts towards the diploma of "Aktuar SAV". For the latter, see details under www.actuaries.ch. Prerequisites: knowledge of probability theory, statistics and applied stochastic processes. | |||||

401-3928-00L | Reinsurance Analytics | W | 4 KP | 2V | P. Antal, P. Arbenz | |

Kurzbeschreibung | This course provides an actuarial introduction to reinsurance. The objective is to understand the fundamentals of risk transfer through reinsurance, and the mathematical models for extreme events such as natural or man-made catastrophes. The lecture covers reinsurance contracts, Experience and Exposure pricing, natural catastrophe modelling, solvency regulation, and alternative risk transfer | |||||

Lernziel | This course provides an introduction to reinsurance from an actuarial point of view. The objective is to understand the fundamentals of risk transfer through reinsurance, and the mathematical approaches associated with low frequency high severity events such as natural or man-made catastrophes. Topics covered include: - Reinsurance Contracts and Markets: Different forms of reinsurance, their mathematical representation, history of reinsurance, and lines of business. - Experience Pricing: Modelling of low frequency high severity losses based on historical data, and analytical tools to describe and understand these models - Exposure Pricing: Loss modelling based on exposure or risk profile information, for both property and casualty risks - Natural Catastrophe Modelling: History, relevance, structure, and analytical tools used to model natural catastrophes in an insurance context - Solvency Regulation: Regulatory capital requirements in relation to risks, effects of reinsurance thereon, and differences between the Swiss Solvency Test and Solvency 2 - Alternative Risk Transfer: Alternatives to traditional reinsurance such as insurance linked securities and catastrophe bonds | |||||

Inhalt | This course provides an introduction to reinsurance from an actuarial point of view. The objective is to understand the fundamentals of risk transfer through reinsurance, and the mathematical approaches associated with low frequency high severity events such as natural or man-made catastrophes. Topics covered include: - Reinsurance Contracts and Markets: Different forms of reinsurance, their mathematical representation, history of reinsurance, and lines of business. - Experience Pricing: Modelling of low frequency high severity losses based on historical data, and analytical tools to describe and understand these models - Exposure Pricing: Loss modelling based on exposure or risk profile information, for both property and casualty risks - Natural Catastrophe Modelling: History, relevance, structure, and analytical tools used to model natural catastrophes in an insurance context - Solvency Regulation: Regulatory capital requirements in relation to risks, effects of reinsurance thereon, and differences between the Swiss Solvency Test and Solvency 2 - Alternative Risk Transfer: Alternatives to traditional reinsurance such as insurance linked securities and catastrophe bonds | |||||

Skript | Slides, lecture notes, and references to literature will be made available. | |||||

Voraussetzungen / Besonderes | Basic knowledge in statistics, probability theory, and actuarial techniques | |||||

401-4889-00L | Mathematical Finance | W | 11 KP | 4V + 2U | M. Schweizer | |

Kurzbeschreibung | Advanced course on mathematical finance: - semimartingales and general stochastic integration - absence of arbitrage and martingale measures - fundamental theorem of asset pricing - option pricing and hedging - hedging duality - optimal investment problems - additional topics | |||||

Lernziel | Advanced course on mathematical finance, presupposing good knowledge in probability theory and stochastic calculus (for continuous processes) | |||||

Inhalt | This is an advanced course on mathematical finance for students with a good background in probability. We want to give an overview of main concepts, questions and approaches, and we do this mostly in continuous-time models. Topics include - semimartingales and general stochastic integration - absence of arbitrage and martingale measures - fundamental theorem of asset pricing - option pricing and hedging - hedging duality - optimal investment problems - and probably others | |||||

Skript | The course is based on different parts from different books as well as on original research literature. Lecture notes will not be available. | |||||

Literatur | (will be updated later) | |||||

Voraussetzungen / Besonderes | Prerequisites are the standard courses - Probability Theory (for which lecture notes are available) - Brownian Motion and Stochastic Calculus (for which lecture notes are available) Those students who already attended "Introduction to Mathematical Finance" will have an advantage in terms of ideas and concepts. This course is the second of a sequence of two courses on mathematical finance. The first course "Introduction to Mathematical Finance" (MF I), 401-3888-00, focuses on models in finite discrete time. It is advisable that the course MF I is taken prior to the present course, MF II. For an overview of courses offered in the area of mathematical finance, see Link. | |||||

401-8905-00L | Financial Engineering (University of Zurich)Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: MFOEC200 Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/mobilitaet.html | W | 6 KP | 4G | Uni-Dozierende | |

Kurzbeschreibung | This lecture is intended for students who would like to learn more on equity derivatives modelling and pricing. | |||||

Lernziel | Quantitative models for European option pricing (including stochastic volatility and jump models), volatility and variance derivatives, American and exotic options. | |||||

Inhalt | After introducing fundamental concepts of mathematical finance including no-arbitrage, portfolio replication and risk-neutral measure, we will present the main models that can be used for pricing and hedging European options e.g. Black- Scholes model, stochastic and jump-diffusion models, and highlight their assumptions and limitations. We will cover several types of derivatives such as European and American options, Barrier options and Variance- Swaps. Basic knowledge in probability theory and stochastic calculus is required. Besides attending class, we strongly encourage students to stay informed on financial matters, especially by reading daily financial newspapers such as the Financial Times or the Wall Street Journal. | |||||

Skript | Script. | |||||

Voraussetzungen / Besonderes | Basic knowledge of probability theory and stochastic calculus. Asset Pricing. | |||||

851-0252-13L | Network ModelingParticularly suitable for students of D-INFK | W | 3 KP | 2V | C. Stadtfeld, V. Amati | |

Kurzbeschreibung | Network Science is a distinct domain of data science that focuses on relational systems. Various models have been proposed to describe structures and dynamics of networks. Statistical and numerical methods have been developed to fit these models to empirical data. Emphasis is placed on the statistical analysis of (social) systems and their connection to social theories and data sources. | |||||

Lernziel | Students will be able to develop hypotheses that relate to the structures and dynamics of (social) networks, and tests those by applying advanced statistical network methods such as stochastic actor-oriented models (SAOMs) and exponential random graph models (ERGMs). Students will be able to explain and compare various network models, and develop an understanding how those can be fit to empirical data. This will enable them to independently address research questions from various social science fields. | |||||

851-0252-17L | Network Analysis Lab (with Exercises) Only for Data Science MSc. | W | 5 KP | 2V + 1U | U. Brandes | |

Kurzbeschreibung | This is a voluntary supplement for the course Network Analysis. | |||||

Lernziel | Deeper understanding of mathematical principles and practical applications of the methods underlying network analysis. | |||||

851-0735-09L | Workshop & Lecture Series on the Law & Economics of Innovation | W | 2 KP | 2S | S. Bechtold, H. Gersbach, A. Heinemann | |

Kurzbeschreibung | This series is a joint project by ETH Zurich and the University of Zurich. It provides an overview of interdisciplinary research on intellectual property, innovation, antitrust and technology policy. Scholars from law, economics, management and related fields give a lecture and/or present their current research. All speakers are internationally well-known experts from Europe, the U.S. and beyond. | |||||

Lernziel | After the workshop and lecture series, participants should be acquainted with interdisciplinary approaches towards intellectual property, innovation, antitrust and technology policy research. They should also have an overview of current topics of international research in these areas. | |||||

Inhalt | The workshop and lecture series will present a mix of speakers who represent the wide range of current social science research methods applied to intellectual property, innovation, antitrust policy and technology policy issues. In particular, theoretical models, empirical and experimental research as well as legal research methods will be represented. | |||||

Skript | Papers discussed in the workshop and lecture series are posted in advance on the course web page. | |||||

Literatur | William Landes / Richard Posner, The Economic Structure of Intellectual Property Law, 2003 Suzanne Scotchmer, Innovation and Incentives, 2004 Peter Menell / Suzanne Scotchmer: Intellectual Property Law, in: Polinsky / Shavell (eds.), Handbook of Law and Economics, Volume 2, Amsterdam 2007, pp. 1471-1570 Bronwyn Hall / Nathan Rosenberg (eds.), Handbook of the Economics of Innovation, 2 volumes, Amsterdam 2010 Bronwyn Hall / Dietmar Harhoff, Recent Research on the Economics of Patents, 2011 Robert Litan (ed.), Handbook on Law, Innovation and Growth, Cheltenham 2011 Paul Belleflamme / Martin Peitz, Industrial Organization: Markets and Strategies, Cambridge, 2nd edition 2015 Einer Elhauge / Damien Geradin, Global Competition Law and Economics, 2nd edition 2011 Martin Peitz / Joel Waldfogel, The Oxford Handbook of the Digital Economy, Oxford 2012 September 2013 issue of the Journal of Industrial Economics, available at http://onlinelibrary.wiley.com/doi/10.1111/joie.2013.61.issue-3/issuetoc Stefan Bechtold, Law and Economics of Copyright and Trademark on the Internet, in: Durlauf/Blume (eds.), The New Palgrave Dictionary of Economics, online edition, Palgrave Macmillan, 2013, available at http://www.dictionaryofeconomics.com/article?id=pde2013_L000245 Robert Merges, Economics of Intellectual Property Law, in Parisi (ed.), Oxford Handbook of Law & Economics, Volume 2, 2017 | |||||

Data Science Projektkurs | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

263-3300-00L | Data Science Lab Only for Data Science MSc. | O | 14 KP | 9P | A. Krause, C. Zhang | |

Kurzbeschreibung | In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc. | |||||

Lernziel | The goal of this class if for students to gain experience of dealing with data science and machine learning applications "in the wild". Students are expected to go through the full process starting from data cleaning, modeling, execution, debugging, error analysis, and quality/performance refinement. | |||||

Voraussetzungen / Besonderes | Prerequisites: At least 8 KP must have been obtained under Data Analysis and at least 8 KP must have been obtained under Data Management and Processing. | |||||

Seminar | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

252-5051-00L | Advanced Topics in Machine Learning Number of participants limited to 40. 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 KP | 2S | J. M. Buhmann, A. Krause, G. Rätsch | |

Kurzbeschreibung | In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning. | |||||

Lernziel | The seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations. | |||||

Inhalt | The seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models. | |||||

Literatur | The papers will be presented in the first session of the seminar. | |||||

263-3504-00L | Hardware Acceleration for Data Processing 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 KP | 2S | G. Alonso, T. Hoefler, C. Zhang | |

Kurzbeschreibung | The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular. | |||||

Lernziel | The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular. | |||||

Inhalt | The general application areas are big data and machine learning. The systems covered will include systems from computer architecture, high performance computing, data appliances, and data centers. | |||||

Voraussetzungen / Besonderes | Students taking this seminar should have the necessary background in systems and low level programming. | |||||

GESS Wissenschaft im Kontext | ||||||

» Empfehlungen aus dem Bereich Wissenschaft im Kontext (Typ B) für das D-INFK | ||||||

» siehe Studiengang Wissenschaft im Kontext: Sprachkurse ETH/UZH | ||||||

» siehe Studiengang Wissenschaft im Kontext: Typ A: Förderung allgemeiner Reflexionsfähigkeiten | ||||||

Master-Arbeit | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

261-0800-00L | Master's ThesisZur Master-Arbeit wird nur zugelassen, wer: das Bachelor-Studium erfolgreich abgeschlossen hat; allfällige Auflagen für die Zulassung zum Studiengang erfüllt hat in der Kategorie "Kernfächer" mindestens 50 KP erworben hat, darunter die je minimal erforderlichen 16 KP in den Unterkategorien "Datenanalyse" sowie "Datenmanagement und Datenverarbeitung" und in der Kategorie "Data Science Projektkurs" die erforderlichen 14 KP erworben hat. | O | 30 KP | 64D | Professor/innen | |

Kurzbeschreibung | ||||||

Lernziel |

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