# Suchergebnis: Katalogdaten im Frühjahrssemester 2019

Data Science Master | ||||||

Interdisziplinäre Wahlfächer | ||||||

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

101-0478-00L | Measurement and Modelling of Travel Behaviour | W | 6 KP | 4G | K. W. Axhausen | |

Kurzbeschreibung | Comprehensive introduction to survey methods in transport planning and modeling of travel behavior, using advanced discrete choice models. | |||||

Lernziel | Enabling the student to understand and apply the various measurement approaches and models of modelling travel behaviour. | |||||

Inhalt | Behavioral model and measurement; travel diary, design process, hypothetical markets, discrete choice model, parameter estimation, pattern of travel behaviour, market segments, simulation, advanced discrete choice models | |||||

Skript | Various papers and notes are distributed during the course. | |||||

103-0228-00L | Multimedia CartographyVoraussetzung: Erfolgreicher Abschluss der Lerneinheit Cartography III (103-0227-00L). | W | 4 KP | 3G | H.‑R. Bär, R. Sieber | |

Kurzbeschreibung | Focus of this course is on the realization of an atlas project in a small team. During the first part of the course, the necessary organizational, creative and technological basics will be provided. At the end of the course, the interactive atlas projects will be presented by the team members. | |||||

Lernziel | The goal of this course is to provide the students the theoretical background, knowledge and practical skills necessary to plan, design and create an interactive Web atlas based on modern Web technologies. | |||||

Inhalt | This course will cover the following topics: - Web map design - Project management - Graphical user interfaces in Web atlases - Interactions in map and atlas applications - Web standards - Programming interactive Web applications - Use of software libraries - Cartographic Web services - Code repository - Copyright and the Internet | |||||

Skript | Lecture notes and additional material are available on Moodle. | |||||

Literatur | - Cartwright, William; Peterson, Michael P. and Georg Gartner (2007); Multimedia Cartography, Springer, Heidelberg | |||||

Voraussetzungen / Besonderes | Prerequisites: Successful completion of Cartography III (103-0227-00L). Previous knowledge in Web programming. The students are expected to - present their work in progress on a regular basis - present their atlas project at the end of the course - keep records of all the work done - document all individual contributions to the project | |||||

103-0247-00L | Mobile GIS and Location-Based Services | W | 5 KP | 4G | P. Kiefer | |

Kurzbeschreibung | The course introduces students to the theoretical and technological background of mobile geographic information systems and location-based services. In lab sessions students acquire competences in mobile GIS design and implementation. | |||||

Lernziel | Students will - learn about the implications of mobility on GIS - get a detailed overview on research fields related to mobile GIS - get an overview on current mobile GIS and LBS technology, and learn how to assess new technologies in this fast-moving field - achieve an integrated view of Geospatial Web Services and mobile GIS - acquire competences in mobile GIS design and implementation | |||||

Inhalt | - LBS and mobile GIS: architectures, market, applications, and application development - Development for Android - Introduction to augmented reality development (HoloLens) - Mobile decision-making, context, personalization, and privacy - Mobile human computer interaction and user interfaces - Mobile behavior interpretation | |||||

Voraussetzungen / Besonderes | Elementary programming skills (Java) | |||||

103-0255-01L | Geodatenanalyse | W | 2 KP | 2G | R. Buffat | |

Kurzbeschreibung | Die Lehrveranstaltung behandelt weiterführende Methoden der Geodatenanalyse. | |||||

Lernziel | - Verstehen der theoretischen Grundlagen räumlicher Analyseverfahren. - Verstehen und Anwenden von Methoden zur raumbezogenen Datenanalyse. - Erkennen häufiger Fehlerquellen bei der Geodatenanalyse. - Vertiefende praktische Kenntnisse in der Anwendung entsprechender GIS-Tools. - Erlernen der Fähigkeit, raumbezogene Probleme jenseits klassischer GIS Software zu lösen. | |||||

Inhalt | In der Lehrveranstaltung werden weiterführende Methoden räumlicher Analyseverfahren theoretisch behandelt sowie anhand von Übungsaufgaben angewendet. | |||||

Skript | kein Skript. | |||||

Literatur | Eine Literaturliste wird in der Lehrveranstaltung zur Verfügung gestellt. | |||||

Voraussetzungen / Besonderes | Voraussetzungen: Basiswissen im Bereich der Geoinformationstechnologien und der Verwendung von Geoinformationssystemen entsprechend den Vorlesungen GIS I und GIS II im Bachelor-Studiengang Geomatik und Planung. | |||||

227-0945-10L | Cell and Molecular Biology for Engineers IIThis course is part II of a two-semester course. Knowledge of part I is required. | W | 3 KP | 2G | C. Frei | |

Kurzbeschreibung | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||

Lernziel | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||

Inhalt | Lectures will include the following topics: DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development and stem cells. In addition, 4 journal clubs will be held, where recent publications will be discussed (2 journal clubs in part I and 2 journal clubs in part II). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 40% for the final grade. | |||||

Skript | Scripts of all lectures will be available. | |||||

Literatur | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Morgan, Raff, Roberts, and Walter. | |||||

227-0391-00L | Medical Image AnalysisBasic knowledge of computer vision would be helpful. | W | 3 KP | 2G | E. Konukoglu, M. A. Reyes Aguirre, C. Tanner | |

Kurzbeschreibung | It is the objective of this lecture to introduce the basic concepts used in Medical Image Analysis. In particular the lecture focuses on shape representation schemes, segmentation techniques, machine learning based predictive models and various image registration methods commonly used in Medical Image Analysis applications. | |||||

Lernziel | This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas. | |||||

Voraussetzungen / Besonderes | Prerequisites: Basic concepts of mathematical analysis and linear algebra. Preferred: Basic knowledge of computer vision and machine learning would be helpful. The course will be held in English. | |||||

261-5113-00L | Computational Challenges in Medical Genomics Number of participants limited to 20. | W | 2 KP | 2S | A. Kahles, G. Rätsch | |

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

Inhalt | 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. | |||||

Voraussetzungen / Besonderes | Knowledge of algorithms and data structures and interest in applications in genomics and computational biomedicine. | |||||

261-5120-00L | Machine Learning for Health Care Number of participants limited to 78. Previously called Computational Biomedicine II | W | 4 KP | 3P | G. Rätsch | |

Kurzbeschreibung | The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems. | |||||

Lernziel | During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in biomedicine, discuss the main challenges they present and their current technical solutions. | |||||

Inhalt | The course will consist of four topic clusters that will cover the most relevant applications of ML in Biomedicine: 1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc. 2) Medical notes: Vast amount of medical observations are stored in the form of free text, we will analyze stategies for extracting knowledge from them. 3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them. 4) Genomics data: ML in genomics is still an emerging subfield, but given that genomics data are arguably the most extensive and complex datasets that can be found in biomedicine, it is expected that many relevant ML applications will arise in the near future. We will review and discuss current applications and challenges. | |||||

Voraussetzungen / Besonderes | Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line Relation to Course 261-5100-00 Computational Biomedicine: This course is a continuation of the previous course with new topics related to medical data and machine learning. The format of Computational Biomedicine II will also be different. It is helpful but not essential to attend Computational Biomedicine before attending Computational Biomedicine II. | |||||

262-0200-00L | Bayesian Phylodynamics | W | 4 KP | 2G + 2A | T. Stadler, T. Vaughan | |

Kurzbeschreibung | How fast was Ebola spreading in West Africa? Where and when did the epidemic outbreak start? How can we construct the phylogenetic tree of great apes, and did gene flow occur between different apes? Students will be able to perform their own phylodynamic analysis of genetic sequencing and independent data analysis to characterize future epidemic outbreaks or reconstruct parts of the tree of life. | |||||

Lernziel | Attendees will extend their knowledge of Bayesian phylodynamics obtained in the “Computational Biology” class (636-0017-00L) and will learn how to apply this theory to real world data. The main theoretical concepts introduced are: * Bayesian statistics * Phylogenetic and phylodynamic models * Markov Chain Monte Carlo methods Attendees will apply these concepts to a number of applications yielding biological insight into: * Epidemiology * Pathogen evolution * Macroevolution of species | |||||

Inhalt | In the first part of the semester, in each week, we will first present the theoretical concepts of Bayesian phylodynamics. The presentation will be followed by attendees using the software package BEAST v2 to apply these theoretical concepts to empirical data. We use previously published datasets on e.g. Ebola, Zika, Yellow Fever, Apes, and Penguins for analysis. Examples of these practical tutorials are available on https://taming-the-beast.org/. In the second part of the semester, the students choose an empirical dataset of genetic sequencing data and possibly some non-genetic metadata. They then design and conduct a research project in which they perform Bayesian phylogenetic analyses of their dataset. The weekly class is intended to discuss and monitor progress and to address students’ questions very interactively. At the end of the semester, the students present their research project in an oral presentation. The content of the presentation, the style of the presentation, and the performance in answering the questions after the presentation will be marked. | |||||

Skript | Lecture slides will be available on moodle. | |||||

Literatur | The following books provide excellent background material: • Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST. • Yang, Z. 2014. Molecular Evolution: A Statistical Approach. • Felsenstein, J. 2003. Inferring Phylogenies. The tutorials in this course are based on our Summer School “Taming the BEAST”: https://taming-the-beast.org/ | |||||

Voraussetzungen / Besonderes | This class builds upon the content which we taught in the Computational Biology class (636-0017-00L). Attendees must have either taken the Computational Biology class or acquired the content elsewhere. | |||||

636-0702-00L | Statistical Models in Computational Biology | W | 6 KP | 2V + 1U + 2A | N. Beerenwinkel | |

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

Inhalt | 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. | |||||

Skript | no | |||||

Literatur | - 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 | |||||

263-3501-00L | Future Internet Previously called Advanced Computer Networks | W | 6 KP | 1V + 1U + 3A | A. Singla | |

Kurzbeschreibung | This course will discuss recent advances in networking, with a focus on the Internet, with topics ranging from the algorithmic design of applications like video streaming to the likely near-future of satellite-based networking. | |||||

Lernziel | The goals of the course are to build on basic undergraduate-level networking, and provide an understanding of the tradeoffs and existing technology in the design of large, complex networked systems, together with concrete experience of the challenges through a series of lab exercises. | |||||

Inhalt | The focus of the course is on principles, architectures, protocols, and applications used in modern networked systems. Example topics include: - How video streaming services like Netflix work, and research on improving their performance. - How Web browsing could be made faster - How the Internet's protocols are improving - Exciting developments in satellite-based networking (ala SpaceX) - The role of data centers in powering Internet services A series of programming assignments will form a substantial part of the course grade. | |||||

Skript | Lecture slides will be made available at the course Web site: https://ndal.ethz.ch/courses/fi.html | |||||

Literatur | No textbook is required, but there will be regularly assigned readings from research literature, liked to the course Web site: https://ndal.ethz.ch/courses/fi.html. | |||||

Voraussetzungen / Besonderes | An undergraduate class covering the basics of networking, such as Internet routing and TCP. At ETH, Computer Networks (252-0064-00L) and Communication Networks (227-0120-00L) suffice. Similar courses from other universities are acceptable too. | |||||

261-5111-00L | Asset Management: Advanced Investments (University of Zurich)Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: MFOEC207 Beachten Sie die Einschreibungstermine an der UZH: http://www.uzh.ch/studies/application/mobilitaet.html | W | 3 KP | 2V | Uni-Dozierende | |

Kurzbeschreibung | Comprehension and application of advanced portfolio theory | |||||

Lernziel | Comprehension and application of advanced portfolio theory | |||||

Inhalt | The theoretical part of the lecture consists of the topics listed below. - Standard Markowitz Model and Extensions MV Optimization, MV with Liabilities and CAPM. - The Crux with MV Resampling, regression, Black-Litterman, Bayesian, shrinkage, constrained and robust optimization. - Downside and Coherent Risk Measures Definition of risk measures, MV optimization under VaR and ES constraints. - Risk Budgeting Equal risk contribution, most diversified portfolio and other concentration indices - Regime Switching and Asset Allocation An introduction to regime switching models and its intuition. - Strategic Asset Allocation Introducing a continuous-time framework, solving the HJB equation and the classical Merton problem. | |||||

363-1000-00L | Financial Economics | W | 3 KP | 2V | A. Bommier | |

Kurzbeschreibung | This is a theoretical course on the economics of financial decision making, at the crossroads between Microeconomics and Finance. It discusses portfolio choice theory, risk sharing, market equilibrium and asset pricing. | |||||

Lernziel | The objective is to make students familiar with the economics of financial decision making and develop their intuition regarding the determination of asset prices, the notions of optimal risk sharing. However this is not a practical formation for traders. Moreover, the lecture doesn't cover topics such as market irrationality or systemic risk. | |||||

Inhalt | The following topics will be discussed: Introduction to finance and investment planning; Option valuation; Arbitrage; Choice under uncertainty; Portfolio Choice; Risk sharing and insurance; Market equilibrium under symmetric information. | |||||

Literatur | Suggesting readings: 1) "Investments", by Z. Bodie, A. Kane and A. Marcus, for the introductory part of the course (see chapters 20 and 21 in particular). 2) "Finance and the Economics of Uncertainty" by G. Demange and G. Laroque, Blackwell, 2006. 3) "The Economics of Risk and Time", by C. Gollier, and Other readings: - "Intermediate Financial Theory" by J.-P. Danthine and J.B. Donaldson. - Ingersoll, J., E., Theory of Financial Decision Making, Rowman and Littlefield Publishers. - Leroy S and J. Werner, Principles of Financial Economics, Cambridge University Press, 2001 | |||||

Voraussetzungen / Besonderes | Basic mathematical skills needed (calculus, linear algebra, convex analysis). Students must be able to solve simple optimization problems (e.g. Lagrangian methods). Some knowledge in microeconomics would help but is not compulsory. The bases will be covered in class. | |||||

401-3629-00L | Quantitative Risk Management | W | 4 KP | 2V + 1U | P. Cheridito | |

Kurzbeschreibung | This course introduces methods from probability theory and statistics that can be used to model financial risks. Topics addressed include loss distributions, risk measures, extreme value theory, multivariate models, copulas, dependence structures and operational risk. | |||||

Lernziel | The goal is to learn the most important methods from probability theory and statistics used in financial risk modeling. | |||||

Inhalt | 1. Introduction 2. Basic Concepts in Risk Management 3. Empirical Properties of Financial Data 4. Financial Time Series 5. Extreme Value Theory 6. Multivariate Models 7. Copulas and Dependence 8. Operational Risk | |||||

Skript | Course material is available on https://people.math.ethz.ch/~patrickc/qrm | |||||

Literatur | Quantitative Risk Management: Concepts, Techniques and Tools AJ McNeil, R Frey and P Embrechts Princeton University Press, Princeton, 2015 (Revised Edition) http://press.princeton.edu/titles/10496.html | |||||

Voraussetzungen / Besonderes | The course corresponds to the Risk Management requirement for the SAA ("Aktuar SAV Ausbildung") as well as for the Master of Science UZH-ETH in Quantitative Finance. | |||||

401-3888-00L | Introduction to Mathematical Finance Ein verwandter Kurs ist 401-3913-01L Mathematical Foundations for Finance (3V+2U, 4 ECTS-KP). Obwohl beide Kurse unabhängig voneinander belegt werden können, darf nur einer ans gesamte Mathematik-Studium (Bachelor und Master) angerechnet werden. | W | 10 KP | 4V + 1U | M. Larsson | |

Kurzbeschreibung | This is an introductory course on the mathematics for investment, hedging, portfolio management, asset pricing and financial derivatives in discrete-time financial markets. We discuss arbitrage, completeness, risk-neutral pricing and utility maximisation. We prove the fundamental theorem of asset pricing and the hedging duality theorems, and also study convex duality in utility maximization. | |||||

Lernziel | This is an introductory course on the mathematics for investment, hedging, portfolio management, asset pricing and financial derivatives in discrete-time financial markets. We discuss arbitrage, completeness, risk-neutral pricing and utility maximisation, and maybe other topics. We prove the fundamental theorem of asset pricing and the hedging duality theorems in discrete time, and also study convex duality in utility maximization. | |||||

Inhalt | This course focuses on discrete-time financial markets. It presumes a knowledge of measure-theoretic probability theory (as taught e.g. in the course "Probability Theory"). The course is offered every year in the Spring semester. This course is the first of a sequence of two courses on mathematical finance. The second course "Mathematical Finance" (MF II), 401-4889-00, focuses on continuous-time models. It is advisable that the present course, MF I, is taken prior to MF II. For an overview of courses offered in the area of mathematical finance, see Link. | |||||

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

Literatur | Literature: Michael U. Dothan, "Prices in Financial Markets", Oxford University Press Hans Föllmer and Alexander Schied, "Stochastic Finance: An Introduction in Discrete Time", de Gruyter Marek Capinski and Ekkehard Kopp, "Discrete Models of Financial Markets", Cambridge University Press Robert J. Elliott and P. Ekkehard Kopp, "Mathematics of Financial Markets", Springer | |||||

Voraussetzungen / Besonderes | A related course is "Mathematical Foundations for Finance" (MFF), 401-3913-01. Although both courses can be taken independently of each other, only one will be given credit points for the Bachelor and the Master degree. In other words, it is also not possible to earn credit points with one for the Bachelor and with the other for the Master degree. This course is the first of a sequence of two courses on mathematical finance. The second course "Mathematical Finance" (MF II), 401-4889-00, focuses on continuous-time models. It is advisable that the present course, MF I, is taken prior to MF II. For an overview of courses offered in the area of mathematical finance, see Link. | |||||

401-3936-00L | Data Analytics for Non-Life Insurance Pricing | W | 4 KP | 2V | C. M. Buser, M. V. Wüthrich | |

Kurzbeschreibung | We study statistical methods in supervised learning for non-life insurance pricing such as generalized linear models, generalized additive models, Bayesian models, neural networks, classification and regression trees, random forests, gradient boosting machines and support vector machines. | |||||

Lernziel | The student is familiar with classical actuarial pricing methods as well as with modern machine learning methods for insurance pricing and prediction. | |||||

Inhalt | We present the following chapters: - generalized linear models (GLMs) - generalized additive models (GAMs) - neural networks - credibility theory - classification and regression trees (CARTs) - bagging, random forests and boosting | |||||

Skript | The lecture notes are available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2870308 | |||||

Voraussetzungen / Besonderes | This course will be held in English and counts towards the diploma of "Aktuar SAV". For the latter, see details under www.actuaries.ch Good knowledge in probability theory, stochastic processes and statistics is assumed. | |||||

401-4658-00L | Computational Methods for Quantitative Finance: PDE Methods | W | 6 KP | 3V + 1U | L. Herrmann, K. Kirchner | |

Kurzbeschreibung | Introduction to principal methods of option pricing. Emphasis on PDE-based methods. Prerequisite MATLAB programming and knowledge of numerical mathematics at ETH BSc level. | |||||

Lernziel | Introduce the main methods for efficient numerical valuation of derivative contracts in a Black Scholes as well as in incomplete markets due Levy processes or due to stochastic volatility models. Develop implementation of pricing methods in MATLAB. Finite-Difference/ Finite Element based methods for the solution of the pricing integrodifferential equation. | |||||

Inhalt | 1. Review of option pricing. Wiener and Levy price process models. Deterministic, local and stochastic volatility models. 2. Finite Difference Methods for option pricing. Relation to bi- and multinomial trees. European contracts. 3. Finite Difference methods for Asian, American and Barrier type contracts. 4. Finite element methods for European and American style contracts. 5. Pricing under local and stochastic volatility in Black-Scholes Markets. 6. Finite Element Methods for option pricing under Levy processes. Treatment of integrodifferential operators. 7. Stochastic volatility models for Levy processes. 8. Techniques for multidimensional problems. Baskets in a Black-Scholes setting and stochastic volatility models in Black Scholes and Levy markets. 9. Introduction to sparse grid option pricing techniques. | |||||

Skript | There will be english, typed lecture notes as well as MATLAB software for registered participants in the course. | |||||

Literatur | R. Cont and P. Tankov : Financial Modelling with Jump Processes, Chapman and Hall Publ. 2004. Y. Achdou and O. Pironneau : Computational Methods for Option Pricing, SIAM Frontiers in Applied Mathematics, SIAM Publishers, Philadelphia 2005. D. Lamberton and B. Lapeyre : Introduction to stochastic calculus Applied to Finance (second edition), Chapman & Hall/CRC Financial Mathematics Series, Taylor & Francis Publ. Boca Raton, London, New York 2008. J.-P. Fouque, G. Papanicolaou and K.-R. Sircar : Derivatives in financial markets with stochastic volatility, Cambridge Univeristy Press, Cambridge, 2000. N. Hilber, O. Reichmann, Ch. Schwab and Ch. Winter: Computational Methods for Quantitative Finance, Springer Finance, Springer, 2013. | |||||

401-8915-00L | Advanced Financial Economics (University of Zurich)Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: MFOEC206 Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/mobilitaet.html | W | 6 KP | 4G | Uni-Dozierende | |

Kurzbeschreibung | Portfolio Theory, CAPM, Financial Derivatives, Incomplete Markets, Corporate Finance, Behavioural Finance, Evolutionary Finance | |||||

Lernziel | Students should get familiar with the cornerstones of modern financial economics. | |||||

Voraussetzungen / Besonderes | This course replaces "Advanced Financial Economics" (MFOEC105), which will be discontinued. Students who have taken "Advanced Financial Economics" (MFOEC105) in the past, are not allowed to book this course "Advanced Financial Economics" (MFOEC206). There will be a podcast for this lecture. | |||||

701-0412-00L | Klimasysteme | W | 3 KP | 2G | R. Knutti, I. Medhaug | |

Kurzbeschreibung | Die wichtigsten physikalischen Komponenten des Klimasystems und deren Wechselwirkungen werden eingeführt. Vor dem Hintergrund der Klimageschichte - und variabilität werden die Mechanismen des anthropogenen Klimawandels analysiert. Absolvierende des Kurses sind in der Lage, einfache Problemstellungen aus dem Bereich der Klimasysteme zu identifizieren und erläutern. | |||||

Lernziel | Studierende können: - die wichtigsten physikalischen Komponenten des goblaben Klimasystems beschreiben und ihre Wechselwirkungen skizzieren. - die Mechanismen des anthropogenen Klimawandels erklären. einfache Problemstellungen aus dem Bereich der Klimasysteme identifizieren und erläutern. | |||||

Skript | Kopien der Folien werden elektronisch zur Verfuegung gestellt. | |||||

Literatur | Eine vollständige Literaturliste wird abgegeben. Insbesondere empfohlen sind: - Hartmann, D., 2016: Global Physical Climatology. Academic Press, London, 485 pp. - Peixoto, J.P. and A.H. Oort, 1992: Physics of Climate. American Institute of Physics, New York, 520 pp. | |||||

Voraussetzungen / Besonderes | Dozierende: Reto Knutti, mehrere Vorträge zu Spezialthemen von anderen Dozenten Unterrichtssprache: deutsch Sprache der Folien: englisch | |||||

701-1216-00L | Numerical Modelling of Weather and Climate | W | 4 KP | 3G | C. Schär, N. Ban | |

Kurzbeschreibung | The course provides an introduction to weather and climate models. It discusses how these models are built addressing both the dynamical core and the physical parameterizations, and it provides an overview of how these models are used in numerical weather prediction and climate research. As a tutorial, students conduct a term project and build a simple atmospheric model using the language PYTHON. | |||||

Lernziel | At the end of this course, students understand how weather and climate models are formulated from the governing physical principles, and how they are used for climate and weather prediction purposes. | |||||

Inhalt | The course provides an introduction into the following themes: numerical methods (finite differences and spectral methods); adiabatic formulation of atmospheric models (vertical coordinates, hydrostatic approximation); parameterization of physical processes (e.g. clouds, convection, boundary layer, radiation); atmospheric data assimilation and weather prediction; predictability (chaos-theory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction. Hands-on experience with simple models will be acquired in the tutorials. | |||||

Skript | Slides and lecture notes will be made available at Link | |||||

Literatur | List of literature will be provided. | |||||

Voraussetzungen / Besonderes | Prerequisites: to follow this course, you need some basic background in atmospheric science, numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) as well as experience in programming. Previous experience with PYTHON is useful but not required. | |||||

701-1226-00L | Inter-Annual Phenomena and Their Prediction | W | 2 KP | 2G | C. Appenzeller | |

Kurzbeschreibung | This course provides an overview of the current ability to understand and predict intra-seasonal and inter-annual climate variability in the tropical and extra-tropical region and provides insights on how operational weather and climate services are organized. | |||||

Lernziel | Students will acquire an understanding of the key atmosphere and ocean processes involved, will gain experience in analyzing and predicting sub-seasonal to inter-annual variability and learn how operational weather and climate services are organised and how scientific developments can improve these services. | |||||

Inhalt | The course covers the following topics: Part 1: - Introduction, some basic concepts and examples of sub-seasonal and inter-annual variability - Weather and climate data and the statistical concepts used for analysing inter-annual variability (e.g. correlation analysis, teleconnection maps, EOF analysis) Part 2: - Inter-annual variability in the tropical region (e.g. ENSO, MJO) - Inter-annual variability in the extra-tropical region (e.g. Blocking, NAO, PNA, regimes) Part 3: - Prediction of inter-annual variability (statistical methods, ensemble prediction systems, monthly and seasonal forecasts, seamless forecasts) - Verification and interpretation of probabilistic forecast systems - Climate change and inter-annual variability Part 4: - Challenges for operational weather and climate services - Role of weather and climate extremes - Early warning systems - A visit to the forecasting centre of MeteoSwiss | |||||

Skript | A pdf version of the slides will be available at http://www.iac.ethz.ch/edu/courses/master/modules/interannual-phenomena.html | |||||

Literatur | References are given during the lecture. | |||||

701-1252-00L | Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation | W | 3 KP | 2V + 1U | D. N. Bresch, R. Knutti | |

Kurzbeschreibung | The course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation. | |||||

Lernziel | Students will acquire knowledge in uncertainty and risk quantification (probabilistic modelling) and an understanding of the economics of climate adaptation. They will become able to construct their own uncertainty and risk assessment models (in Python), hence basic understanding of scientific programming forms a prerequisite of the course. | |||||

Inhalt | The first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed – leading to informed decisions to optimally allocate resources. Such pre-emptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society. The course provides an introduction to the following themes: 1) basics of probabilistic modelling and quantification of uncertainty from global climate change to local impacts of extreme events 2) methods to optimize and constrain model parameters using observations 3) risk management from identification (perception) and understanding (assessment, modelling) to actions (prevention, preparation, response, recovery, risk transfer) 4) basics of economic evaluation, economic decision making in the presence of climate risks and pre-emptive risk management to optimally allocate resources | |||||

Skript | Powerpoint slides will be made available | |||||

Literatur | - | |||||

Voraussetzungen / Besonderes | Hands-on experience with probabilistic climate models and risk models will be acquired in the tutorials; hence basic understanding of scientific programming forms a prerequisite of the course. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required. Examination: graded tutorials during the semester (benotete Semesterleistung) | |||||

851-0252-06L | Introduction to Social Networks: Theory, Methods and ApplicationsThis course is intended for students interested in data analysis and with basic knowledge of inferential statistics. | W | 3 KP | 2G | C. Stadtfeld, T. Elmer, A. Vörös | |

Kurzbeschreibung | Humans are connected by various social relations. When aggregated, we speak of social networks. This course discusses how social networks are structured, how they change over time and how they affect the individuals that they connect. It integrates social theory with practical knowledge of cutting-edge statistical methods and applications from a number of scientific disciplines. | |||||

Lernziel | The aim is to enable students to contribute to social networks research and to be discriminating consumers of modern literature on social networks. Students will acquire a thorough understanding of social networks theory (1), practical skills in cutting-edge statistical methods (2) and their applications in a number of scientific fields (3). In particular, at the end of the course students will - Know the fundamental theories in social networks research (1) - Understand core concepts of social networks and their relevance in different contexts (1, 3) - Be able to describe and visualize networks data in the R environment (2) - Understand differences regarding analysis and collection of network data and other type of survey data (2) - Know state-of-the-art inferential statistical methods and how they are used in R (2) - Be familiar with the core empirical studies in social networks research (2, 3) - Know how network methods can be employed in a variety of scientific disciplines (3) | |||||

363-1091-00L | Social Data Science | W | 3 KP | 2G | D. Garcia Becerra | |

Kurzbeschreibung | Social Data Science is introduced as a set of techniques to analyze human behavior and social interaction through digital traces. The course focuses both on the fundamentals and applications of Data Science in the Social Sciences, including technologies for data retrieval, processing, and analysis with the aim to derive insights that are interpretable from a wider theoretical perspective. | |||||

Lernziel | A successful participant of this course will be able to - understand a wide variety of techniques to retrieve digital trace data from online data sources - store, process, and summarize online data for quantitative analysis - perform statistical analyses to test hypotheses, derive insights, and formulate predictions - implement streamlined software that integrates data retrieval, processing, statistical analysis, and visualization - interpret the results of data analysis with respect to theoretical and testable principles of human behavior - understand the limitations of observational data analysis with respect to data volume, statistical power, and external validity | |||||

Inhalt | Social Data Science (SDS) provides a broad approach to the quantitative analysis of human behavior through digital trace data. SDS integrates the implementation of data retrieval and processing, the application of statistical analysis methods, and the interpretation of results to derive insights of human behavior at high resolutions and large scales. The motivation of SDS stems from theories in the Social Sciences, which are addressed with respect to societal phenomena and formulated as principles that can be tested against empirical data. Data retrieval in SDS is performed in an automated manner, accessing online databases and programming interfaces that capture the digital traces of human behavior. Data processing is computerized with calibrated methods that quantify human behavior, for example constructing social networks or measuring emotional expression. These quantities are used in statistical analyses to both test hypotheses and explore new aspects on human behavior. The course starts with an introduction to Social Data Science and the R statistical language, followed by three content blocks: collective behavior, sentiment analysis, and social network analysis. The course ends with a datathon that sets the starting point of final student projects. The course will cover various examples of the application of SDS: - Search trends to measure information seeking - Popularity and social impact - Evaluation of sentiment analysis techniques - Quantitative analysis of emotions and social media sharing - Twitter social network analysis The lectures include theoretical foundations of the application of digital trace data in the Social Sciences, as well as practical examples of data retrieval, processing, and analysis cases in the R statistical language from a literate programming perspective. The block course contains lectures and exercise sessions during the morning and afternoon of five days. Exercise classes provide practical skills and discuss the solutions to exercises that build on the concepts and methods presented in the previous lectures. | |||||

Skript | The lecture slides will be available on the Moodle platform, for registered students only. | |||||

Literatur | See handouts. Specific literature is provided for download, for registered students only. | |||||

Voraussetzungen / Besonderes | Participants of the course should have some basic background in statistics and programming, and an interest to learn about human behavior from a quantitative perspective. Prior knowledge of advanced R, information retrieval, or information systems is not necessary. Exercise sessions build on technical and theoretical content explained in the lectures. Students need a working laptop with Internet access to perform the guided exercises. Course evaluation is based on the project developed in the last session datathon (50%) and on the final project report (50%). The course takes place between Feb 11th and Feb 15th (both inclusive), from 9:15 to 12:00 and from 13:15 to 16:00. | |||||

227-0395-00L | Neural Systems | W | 6 KP | 2V + 1U + 1A | R. Hahnloser, M. F. Yanik, B. Grewe | |

Kurzbeschreibung | This course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experimental techniques used in studies of animal behavior and underlying neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on efforts to design artificially intelligent systems. | |||||

Lernziel | This course introduces - Methods for monitoring of animal behaviors in complex environments - Information-theoretic principles of behavior - Methods for performing neurophysiological recordings in intact nervous systems - Methods for manipulating the state and activity in selective neuron types - Methods for reconstructing the synaptic networks among neurons - Information decoding from neural populations, - Sensorimotor learning, and - Neurobiological principles for machine learning. | |||||

Inhalt | From active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics. | |||||

Voraussetzungen / Besonderes | Before taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L). As part of the exercises for this class, students are expected to complete a (python) programming project to be defined at the beginning of the semester. | |||||

227-0973-00L | Translational Neuromodeling | W | 8 KP | 3V + 2U + 1A | K. Stephan | |

Kurzbeschreibung | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises. | |||||

Lernziel | To obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data. | |||||

Inhalt | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from psychiatry and psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, for example, dynamic causal models for inferring neuronal mechanisms from neuroimaging data, and hierarchical Bayesian models for inference on cognitive mechanisms from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models. Lecture topics include: 1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics 2. Psychiatric nosology 3. Pathophysiology of psychiatric disease mechanisms 4. Principles of Bayesian inference and generative modeling 5. Variational Bayes (VB) 6. Bayesian model selection 7. Markov Chain Monte Carlo techniques (MCMC) 8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases 9. Generative models of fMRI data 10. Generative models of electrophysiological data 11. Generative models of behavioural data 12. Computational concepts of schizophrenia, depression and autism 13. Model-based predictions about individual patients Practical exercises include mathematical derivations and the implementation of specific models or inference methods. In additional project work, students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is permitted. | |||||

Literatur | See TNU website: https://www.tnu.ethz.ch/en/teaching.html | |||||

Voraussetzungen / Besonderes | Knowledge of principles of statistics, programming skills (MATLAB or Python) | |||||

227-1032-00L | Neuromorphic Engineering II Information für UZH Studierende: Die Lerneinheit kann nur an der ETH belegt werden. Die Belegung des Moduls INI405 ist an der UZH nicht möglich. Beachten Sie die Einschreibungstermine an der ETH für UZH Studierende: Link | W | 6 KP | 5G | T. Delbrück, G. Indiveri, S.‑C. Liu | |

Kurzbeschreibung | This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I". | |||||

Lernziel | Design of a neuromorphic circuit for implementation with CMOS technology. | |||||

Inhalt | This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I". The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester. | |||||

Literatur | S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation. | |||||

Voraussetzungen / Besonderes | Prerequisites: Neuromorphic Engineering I strongly recommended | |||||

227-1034-00L | Computational Vision (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI402 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 6 KP | 2V + 1U | D. Kiper | |

Kurzbeschreibung | This course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision. | |||||

Lernziel | This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered. | |||||

Inhalt | This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered. | |||||

Literatur | Books: (recommended references, not required) 1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997. 2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995. | |||||

851-0739-01L | Building a Robot Judge: Data Science For the LawParticularly suitable for students of D-INFK, D-ITET, D-MTEC | W | 3 KP | 2V | E. Ash | |

Kurzbeschreibung | This course explores the automation of decisions in the legal system. We delve into the tools from natural language processing and machine learning needed to predict judge decision-making and ask whether it is possible -- or even desirable -- to build a robot judge. | |||||

Lernziel | Is a concept of justice what truly separates man from machine? Recent advances in data science have caused many people to reconsider their responses to this question. With expanding digitization of legal data and corpora, alongside rapid developments in natural language processing and machine learning, the prospect arises for automating legal decisions. Data science technologies have the potential to improve legal decisions by making them more efficient and consistent. The benefits to society from this automation could be significant. On the other hand, there are serious risks that automated systems could replicate or amplify existing legal biases and rigidities. This course introduces students to the data science tools that are unlocking legal materials for computational and scientific analysis. We begin with the problem of representing laws as data, with a review of techniques for featurizing texts, extracting legal information, and representing documents as vectors. We explore methods for measuring document similarity and clustering documents based on legal topics or other features. Visualization methods include word clouds and t-SNE plots for spatial relations between documents. We next consider legal prediction problems. Given the evidence and briefs in this case, how will a judge probably decide? How likely is a criminal defendant to commit another crime? How much additional revenue will this new tax law collect? Students will investigate and implement the relevant machine learning tools for making these types of predictions, including regression, classification, and deep neural networks models. We then use these predictions to better understand the operation of the legal system. Under what conditions do judges tend to make errors? Against which types of defendants do parole boards exhibit bias? Which jurisdictions have the most tax loopholes? In a semester project, student groups will conceive and implement a research design for examining this type of empirical research question. Some programming experience in Python is required, and some experience with text mining is highly recommended. | |||||

851-0739-02L | Building a Robot Judge: Data Science for the Law (Course Project)This is the optional course project for "Building a Robot Judge: Data Science for the Law." Please register only if attending the lecture course or with consent of the instructor. Some programming experience in Python is required, and some experience with text mining is highly recommended. | W | 2 KP | 2V | E. Ash | |

Kurzbeschreibung | Students investigate and implement the relevant machine learning tools for making legal predictions, including regression, classification, and deep neural networks models. | |||||

Lernziel | ||||||

Inhalt | Students will investigate and implement the relevant machine learning tools for making legal predictions, including regression, classification, and deep neural networks models. We will use these predictions to better understand the operation of the legal system. In a semester project, student groups will conceive and implement a research design for examining this type of empirical research question. | |||||

851-0740-00L | Big Data, Law, and Policy Number of participants limited to 35 Students will be informed by 3.3.2019 at the latest. | W | 3 KP | 2S | S. Bechtold, T. Roscoe, E. Vayena | |

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

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 | |

Kurzbeschreibung | This seminar provides master students at ETH with the challenging opportunity of working on a real risk modelling and risk management case in close collaboration with a Risk Center Partner Company. For the Spring 2019 Edition the Partner will be Zurich Insurance Group. | |||||

Lernziel | Students work 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 risk-related cases can be complex, cover ambiguities, and may be addressed in more than one way. During the seminar students visit the partners’ headquarters, conduct interviews with members of the management team as well as internal and external experts, and present their results. | |||||

Inhalt | Get a basic understanding of o The insurance and reinsurance business o Risk management and risk modelling o The role of operational risk management Get in contact with industry experts and conduct interviews on the topic. Conduct a small empirical study and present findings to the company | |||||

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 February 15, 2019. The number of participants is limited to 14. |