Suchergebnis: Katalogdaten im Herbstsemester 2019

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
Data Science Master
Interdisziplinäre Wahlfächer
101-0491-00L	Agent Based Modeling in Transportation	W	6 KP	4G	T. J. P. Dubernet, M. Balac
Kurzbeschreibung	This lectures provides a round tour of agent based models for transportation policy analysis. First, it introduces statistical methods to combine heterogeneous data sources in a usable representation of the population. Then, agent based models are described in details, and applied in a case study.
Lernziel	At the end of the course, the students should: - be aware of the various data sources available for mobility behavior analysis - be able to combine those data sources in a coherent representation of the transportation demand - understand what agent based models are, when they are useful, and when they are not - have working knowledge of the MATSim software, and be able to independently evaluate a transportation problem using it
Inhalt	This lecture provides a complete introduction to agent based models for transportation policy analysis. Two important topics are covered: 1) Combination of heterogeneous data sources to produce a representation of the transport system At the center of agent based models and other transport analyses is the synthetic population, a statistically realistic representation of the population and their transport needs. This part will present the most common types of data sources and statistical methods to generate such a population. 2) Use of Agent-Based methods to evaluate transport policies The second part will introduce the agent based paradigm in details, including tradeoffs compared to state-of-practice methods. An important part of the grade will come from a policy analysis to carry with the MATSim open-source software, which is developed at ETH Zurich and TU Berlin and gets used more and more by practitioners, notably the Swiss rail operator SBB.
Literatur	Agent-based modeling in general Helbing, D (2012) Social Self-Organization, Understanding Complex Systems, Springer, Berlin. Heppenstall, A., A. T. Crooks, L. M. See and M. Batty (2012) Agent-Based Models of Geographical Systems, Springer, Dordrecht. MATSim Horni, A., K. Nagel and K.W. Axhausen (eds.) (2016) The Multi-Agent Transport Simulation MATSim, Ubiquity, London (http://www.matsim.org/the-book) Additional relevant readings, mostly scientific articles, will be recommended throughout the course.
Voraussetzungen / Besonderes	There are no strict preconditions in terms of which lectures the students should have previously attended. However, knowledge of basic statistical theory is expected, and experience with at least one high-level programming language (Java, R, Python...) is useful. The course uses Python.
103-0227-00L	Cartography III	W	5 KP	4G	L. Hurni
Kurzbeschreibung	This follow-up course proceeds to a complete Web map project and introduces in 3D and animated cartography.
Lernziel	This course enables students to plan, design and realize interactive Web map projects. The introduction to 3D and animated cartography also provides a general knowledge about animated 3D graphics.
Inhalt	- Web mapping - Data processing - Interaction design - Graphical user interface - 3D cartography - Animated cartography - Video production
Skript	Handouts of the lectures and exercise documents are available on Moodle.
Voraussetzungen / Besonderes	Further information at http://www.karto.ethz.ch/studium/lehrangebot.html
103-0237-00L	GIS III	W	5 KP	3G	M. Raubal
Kurzbeschreibung	The course deals with advanced topics in GIS, such as Business aspects and Legal issues; Geostatistics; Human-Computer Interaction; Cognitive Issues in GIS; Geosensors; and Machine Learning for GIS.
Lernziel	Students will get a detailed overview of advanced GIS topics. They will work on a small project with geosensors in the lab and perform practical tasks relating to Geostatistics and Machine Learning.
Skript	Lecture slides will be made available in digital form.
Literatur	O'Sullivan, D., & Unwin, D. (2010). Geographic Information Analysis (second ed.). Hoboken, New Jersey: Wiley.
103-0778-00L	GIS and Geoinformatics Lab	W	4 KP	3P	M. Raubal
Kurzbeschreibung	Independent study project with (mobile) geoinformation technologies.
Lernziel	Learn how to work with (mobile) geoinformation technologies (including application design and programming).
263-3900-01L	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.
227-0575-00L	Advanced Topics in Communication Networks (Autumn 2019)	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 2019, 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 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 of 2019 (and similarly to the 2018 edition), the class will focus on programmable network data planes and will involve developing network applications on top of the latest generation of programmable network hardware. 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. 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 project to be done in groups of few students (~3 students). The project will involve developing a fully working network application. 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.
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 and neural networks, 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 Generalized Linear Models and Neural Networks 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 introduction to reinsurance from an actuarial perspective. The objective is to understand the fundamentals of risk transfer through reinsurance and 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 insurance linked securities
Lernziel	This course provides an introduction to reinsurance from an actuarial perspective. 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 - Insurance linked securities: Alternative risk transfer techniques such as catastrophe bonds
Inhalt	This course provides an introduction to reinsurance from an actuarial perspective. 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 - Insurance linked securities: Alternative risk transfer techniques such as catastrophe bonds
Skript	Slides and lecture notes 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	J. Teichmann
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 Modeling Particularly suitable for students of D-INFK Students are required to have basic knowledge in inferential statistics, such as regression models.	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.
Voraussetzungen / Besonderes	Students are required to have basic knowledge in inferential statistics and should be familiar with linear and logistic regression models.
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 Paul Belleflamme / Martin Peitz, Industrial Organization: Markets and Strategies, Cambridge, 2nd edition 2015 Robert Merges, Economics of Intellectual Property Law, in Parisi (ed.), Oxford Handbook of Law & Economics, Volume 2, 2017
851-0252-15L	Network Analysis Particularly suitable for students of D-INFK, D-MATH	W	3 KP	2V	U. Brandes
Kurzbeschreibung	Network science is a distinct domain of data science that is characterized by a specific kind of data being studied. While areas of application range from archaeology to zoology, we concern ourselves with social networks for the most part. Emphasis is placed on descriptive and analytic approaches rather than theorizing, modeling, or data collection.
Lernziel	Students will be able to identify and categorize research problems that call for network approaches while appreciating differences across application domains and contexts. They will master a suite of mathematical and computational tools, and know how to design or adapt suitable methods for analysis. In particular, they will be able to evaluate such methods in terms of appropriateness and efficiency.
Inhalt	The following topics will be covered with an emphasis on structural and computational approaches and frequent reference to their suitability with respect to substantive theory: * Empirical Research and Network Data * Macro and Micro Structure * Centrality * Roles * Cohesion
Skript	Lecture notes are distributed via the associated course moodle.
Literatur	* Hennig, Brandes, Pfeffer & Mergel (2012). Studying Social Networks. Campus-Verlag. * Borgatti, Everett & Johnson (2013). Analyzing Social Networks. Sage. * Robins (2015). Doing Social Network Research. Sage. * Brandes & Erlebach (2005). Network Analysis. Springer LNCS 3418. * Wasserman & Faust (1994). Social Network Analysis. Cambridge University Press. * Kadushin (2012). Understanding Social Networks. Oxford University Press.

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 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 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, 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.
363-1100-00L	Risk Case Study Challenge Limited number of participants. Please apply for this course via the official website (www.riskcenter.ethz.ch). Once your application is confirmed, registration in myStudies is possible.	W	3 KP	2S	B. J. Bergmann, A. Bommier, S. Feuerriegel, J. Teichmann
Kurzbeschreibung	This seminar provides master students at ETH with the challenging opportunity of working on a real risk case in close collaboration with a company. For Fall 2019 the Partner will be Credit Suisse and the topic of cases will focus on machine learning applications in finance.
Lernziel	Students work in groups on a real risk-related case of a business relevant topic provided by experts from Risk Center partners. While gaining substantial insights into the risk modeling and management of the industry, students explore the case or problem on their own, working in teams, and develop possible solutions. The cases allow students to use logical problem solving skills with emphasis on evidence and application and involve the integration of scientific knowledge. Typically, the cases can be complex, cover ambiguities, and may be addressed in more than one way. During the seminar, students visit the partners’ headquarters, interact and conduct interviews with risk professionals. The final results will be presented at the partners' headquarters.
Inhalt	Get a basic understanding of o Risk management and risk modelling o Machine learning tools and applications o How to communicate your results to risk professionals For that you work in a group of 4 students together with a Case Manager from the company. In addition you are coached by the Lecturers on specific aspects of machine learning as well as communication and presentation skills.
Voraussetzungen / Besonderes	Please apply for this course via the official website (www.riskcenter.ethz.ch/education/lectures/risk-case-study-challenge-.html). Apply no later than September 13, 2019. The number of participants is limited to 16.
401-3620-69L	Student Seminar in Statistics: The Art of Statistics Maximale Teilnehmerzahl: 24 Hauptsächlich für Studierende der Bachelor- und Master-Studiengänge Mathematik, welche nach der einführenden Lerneinheit 401-2604-00L Wahrscheinlichkeit und Statistik (Probability and Statistics) mindestens ein Kernfach oder Wahlfach in Statistik besucht haben. Das Seminar wird auch für Studierende der Master-Studiengänge Statistik bzw. Data Science angeboten.	W	4 KP	2S	M. H. Maathuis
Kurzbeschreibung	We will study the book "The Art of Statistics: Learning from Data" by David Spiegelhalter. The focus of the book is not so much on technical aspects, but more on concepts, philosophical aspects, statistical thinking and communication. Chapters will be presented by pairs of students, followed by an open discussion with everyone in the class.
Lernziel	We will study roughly one chapter per week from the book "The Art of Statistics: Learning from Data" by David Spiegelhalter. The focus of the book is not so much on technical aspects, but more on concepts, philosophical aspects, statistical thinking and communication. This will also be the focus of the class, but we may occasionally look up additional information from references that are given in the book. Besides improving your statistical thinking, you will practice your self-studying, collaboration and presentation skills.
Literatur	David Spiegelhalter (2019). The Art of Statistics: Learning from Data. UK: Pelican. ISBN: 978-0-241-39863-0
Voraussetzungen / Besonderes	Besides an introductory course in Probability and Statistics, we require one subsequent Statistics course. We also expect some experience with the statistical software R. Topics will be assigned during the first meeting.
401-5680-00L	Foundations of Data Science Seminar	E-	0 KP		P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, G. Rätsch, C. Uhler, S. van de Geer
Kurzbeschreibung	Research colloquium
Lernziel

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