# Search result: Catalogue data in Spring Semester 2019

Data Science Master | ||||||

Interdisciplinary Electives | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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101-0478-00L | Measurement and Modelling of Travel Behaviour | W | 6 credits | 4G | K. W. Axhausen | |

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

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

Content | 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 | |||||

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

103-0228-00L | Multimedia CartographyPrerequisite: Successful completion of Cartography III (103-0227-00L). | W | 4 credits | 3G | H.‑R. Bär, R. Sieber | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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 | |||||

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

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

Prerequisites / Notice | 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 credits | 4G | P. Kiefer | |

Abstract | 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. | |||||

Objective | 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 | |||||

Content | - 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 | |||||

Prerequisites / Notice | Elementary programming skills (Java) | |||||

103-0255-01L | Geodata Analysis | W | 2 credits | 2G | R. Buffat | |

Abstract | The course deals with advanced methods in spatial data analysis. | |||||

Objective | - Understanding the theoretical principles in spatial data analysis. - Understanding and using methods for spatial data analysis. - Detecting common sources of errors in spatial data analysis. - Advanced practical knowledge in using appropriate GIS-tools. | |||||

Content | The course deals with advanced methods in spatial data analysis in theory as well as in practical exercises. | |||||

Lecture notes | no script. | |||||

Literature | A literature list will be provided in the course. | |||||

Prerequisites / Notice | Prerequisites: Basic knowledge in geo information technologies and the use of geographic information systems according to the courses GIS I and GIS II in the bachelor curriculum geomatics and planning. | |||||

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 credits | 2G | C. Frei | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. | |||||

Lecture notes | Scripts of all lectures will be available. | |||||

Literature | "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 credits | 2G | E. Konukoglu, M. A. Reyes Aguirre, C. Tanner | |

Abstract | 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. | |||||

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

Prerequisites / Notice | 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 credits | 2S | A. Kahles, G. Rätsch | |

Abstract | This seminar discusses recent relevant contributions to the fields of computational genomics, algorithmic bioinformatics, statistical genetics and related areas. Each participant will hold a presentation and lead the subsequent discussion. | |||||

Objective | Preparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to succesfully present the key points of existing research work are the same as needed to communicate own research ideas. In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class. | |||||

Content | The topics covered in the seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc. Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are amongst the list of selected papers. | |||||

Prerequisites / Notice | Knowledge of algorithms and data structures and interest in applications in genomics and computational biomedicine. | |||||

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

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. | |||||

Prerequisites / Notice | 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 credits | 2G + 2A | T. Stadler, T. Vaughan | |

Abstract | 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. | |||||

Objective | 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 | |||||

Content | 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. | |||||

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

Literature | 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/ | |||||

Prerequisites / Notice | 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 credits | 2V + 1U + 2A | N. Beerenwinkel | |

Abstract | The course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods. | |||||

Objective | The goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets. | |||||

Content | Graphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises. | |||||

Lecture notes | no | |||||

Literature | - Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252 - Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007. - Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004 | |||||

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

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. | |||||

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

Literature | 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. | |||||

Prerequisites / Notice | 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)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: MFOEC207 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 3 credits | 2V | University lecturers | |

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

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

Content | 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 credits | 2V | A. Bommier | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. | |||||

Literature | 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 | |||||

Prerequisites / Notice | 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 credits | 2V + 1U | P. Cheridito | |

Abstract | 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. | |||||

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

Content | 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 | |||||

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

Literature | 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 | |||||

Prerequisites / Notice | 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 A related course is 401-3913-01L Mathematical Foundations for Finance (3V+2U, 4 ECTS credits). Although both courses can be taken independently of each other, only one will be recognised for credits in the Bachelor and Master degree. In other words, it is not allowed to earn credit points with one for the Bachelor and with the other for the Master degree. | W | 10 credits | 4V + 1U | M. Larsson | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. | |||||

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

Literature | 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 | |||||

Prerequisites / Notice | 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 credits | 2V | C. M. Buser, M. V. Wüthrich | |

Abstract | 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. | |||||

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

Content | 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 | |||||

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

Prerequisites / Notice | 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 credits | 3V + 1U | L. Herrmann, K. Kirchner | |

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

Objective | 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. | |||||

Content | 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. | |||||

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

Literature | 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)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: MFOEC206 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 6 credits | 4G | University lecturers | |

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

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

Prerequisites / Notice | 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 | Climate Systems | W | 3 credits | 2G | R. Knutti, I. Medhaug | |

Abstract | This course introduces the most important physical components of the climate system and their interactions. The mechanisms of anthropogenic climate change are analysed against the background of climate history and variability. Those completing the course will be in a position to identify and explain simple problems in the area of climate systems. | |||||

Objective | Students are able - to describe the most important physical components of the global climate system and sketch their interactions - to explain the mechanisms of anthropogenic climate change - to identify and explain simple problems in the area of climate systems | |||||

Lecture notes | Copies of the slides are provided in electronic form. | |||||

Literature | A comprehensive list of references is provided in the class. Two books are particularly recommended: - 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. | |||||

Prerequisites / Notice | Teaching: Reto Knutti, several keynotes to special topics by other professors Course taught in german, slides in english | |||||

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

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. | |||||

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

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

Prerequisites / Notice | 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. |

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