Search result: Catalogue data in Spring Semester 2018

Data Science Master Information
Interdisciplinary Electives
NumberTitleTypeECTSHoursLecturers
101-0478-00LMeasurement and Modelling of Travel BehaviourW6 credits4GK. W. Axhausen
AbstractComprehensive introduction to survey methods in transport planning and modeling of travel behavior, using advanced discrete choice models.
ObjectiveEnabling the student to understand and apply the various measurement approaches and models of modelling travel behaviour.
ContentBehavioral 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 notesVarious papers and notes are distributed during the course.
Prerequisites / NoticeRequirement: Transport I
103-0228-00LMultimedia Cartography
Prerequisite: Successful completion of Cartography III (103-0227-00L).
W4 credits3GH.‑R. Bär, R. Sieber
AbstractFocus 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.
ObjectiveThe 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.
ContentThis 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 notesLecture notes and additional material are available on Moodle.
Literature- Cartwright, William; Peterson, Michael P. and Georg Gartner (2007); Multimedia Cartography, Springer, Heidelberg
Prerequisites / NoticePrerequisites: 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
103-0247-00LMobile GIS and Location-Based ServicesW5 credits4GP. Kiefer
AbstractThe 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.
ObjectiveStudents 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
- Mobile decision-making, context, personalization, and privacy
- Mobile human computer interaction and user interfaces
- Mobile behavior interpretation
Prerequisites / NoticeElementary programming skills (Java)
103-0255-01LGeodata AnalysisW2 credits2GD. Jonietz
AbstractThe 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.
ContentThe course deals with advanced methods in spatial data analysis in theory as well as in practical exercises.
Lecture notesno script.
LiteratureA literature list will be provided in the course.
Prerequisites / NoticePrerequisites:
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.
227-0945-10LCell and Molecular Biology for Engineers II
This course is part II of a two-semester course.
Knowledge of part I is required.
W3 credits2GC. Frei
AbstractThe 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.
ObjectiveAfter 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.
ContentLectures 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, three journal clubs will be held, where one/two publictions will be discussed. 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 25% for the final grade.
Lecture notesScripts of all lectures will be available.
Literature"Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Morgan, Raff, Roberts, and Walter.
261-5111-00LAsset 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: Link
W3 credits2VUniversity lecturers
AbstractComprehension and application of advanced portfolio theory
ObjectiveComprehension and application of advanced portfolio theory
ContentThe 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.
261-5120-00LComputational Biomedicine IIW4 credits3PG. Rätsch
AbstractThe 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.
ObjectiveDuring 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.
ContentThe 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 / NoticeData 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.
263-3501-00LAdvanced Computer Networks Information W5 credits2V + 2UA. Singla, P. M. Stüdi
AbstractThis course covers a set of advanced topics in computer networks. The focus is on principles, architectures, and protocols used in modern networked systems, such as the Internet and data center networks.
ObjectiveThe 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.
ContentThe focus of the course is on principles, architectures, and protocols used in modern networked systems. Topics include data center network topologies, software defined networking, network function virtualization, flow control and congestion control in data centers, end-point optimizations, and server virtualization.
363-1000-00LFinancial EconomicsW3 credits2VA. Bommier
AbstractThis 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.
ObjectiveThe 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.
ContentThe 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.
LiteratureSuggesting 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 / NoticeBasic 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.
363-1091-00LSocial Data ScienceW3 credits2GD. Garcia Becerra
AbstractSocial 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.
ObjectiveA 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
ContentSocial 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.
Lecture notesThe lecture slides will be available on the Moodle platform, for registered students only.
LiteratureSee handouts. Specific literature is provided for download, for registered students only.
Prerequisites / NoticeParticipants 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 12th and Feb 16th (both inclusive), from 9:15 to 12:00 and from 13:15 to 16:00.
401-3629-00LQuantitative Risk ManagementW4 credits2VP. Cheridito
AbstractThis 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 and dependence structures as well as operational risk.
ObjectiveThe goal is to learn the most important methods from probability theory and statistics used in financial risk modeling.
Content1. 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 notesCourse material is available on Link
LiteratureQuantitative Risk Management: Concepts, Techniques and Tools
AJ McNeil, R Frey and P Embrechts
Princeton University Press, Princeton, 2015 (Revised Edition)
Link
Prerequisites / NoticeThe 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.
401-3888-00LIntroduction to Mathematical Finance Information
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.
W10 credits4V + 1UM. Schweizer
AbstractThis 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.
ObjectiveThis 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.
ContentThis 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 notesThe course is based on different parts from different textbooks as well as on original research literature. Lecture notes will not be available.
LiteratureLiterature:

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 / NoticeNOTE: Due to personal (health) reasons, this course is offered in concentrated form during the second half of the semester. The course will start on *Monday, April 09, 2018*. Some extra information about possible preparation as well as extra references will be posted here later.

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.
401-3936-00LData Analytics for Non-Life Insurance PricingW4 credits2VC. M. Buser, M. V. Wüthrich
AbstractWe 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. Moreover, we present unsupervised learning methods applied to telematics car driving data.
ObjectiveThe student is familiar with classical actuarial pricing methods as well as with modern machine learning methods for insurance pricing and prediction.
ContentWe present the following chapters:
- generalized linear models (GLMs)
- generalized additive models (GAMs)
- credibility theory
- classification and regression trees (CARTs)
- bagging, random forests and boosting
- support vector machines (SVMs)
- unsupervised learning methods
- telematics car driving data
Lecture notesThe lecture notes are available from:
Link
Prerequisites / NoticeThis course will be held in English and counts towards the diploma of "Aktuar SAV".
For the latter, see details under Link

Good knowledge in probability theory, stochastic processes and statistics is assumed.
401-4658-00LComputational Methods for Quantitative Finance: PDE Methods Information Restricted registration - show details W6 credits3V + 1UC. Schwab
AbstractIntroduction to principal methods of option pricing. Emphasis on PDE-based methods. Prerequisite MATLAB programming
and knowledge of numerical mathematics at ETH BSc level.
ObjectiveIntroduce 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.
Content1. 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 notesThere will be english, typed lecture notes as well as MATLAB software for registered participants in the course.
LiteratureR. 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.
Prerequisites / NoticeStart of the lecture: WED, 28 Feb. 2018 (second week of the semester).
401-8915-00LAdvanced 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:
Link
W6 credits4GUniversity lecturers
AbstractPortfolio Theory, CAPM, Financial Derivatives, Incomplete Markets, Corporate Finance, Behavioural Finance, Evolutionary Finance
ObjectiveStudents should get familiar with the cornerstones of modern financial economics.
Prerequisites / NoticeThis 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.
636-0702-00LStatistical Models in Computational BiologyW6 credits2V + 1U + 2AN. Beerenwinkel
AbstractThe 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.
ObjectiveThe 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.
ContentGraphical 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 notesno
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
701-0412-00LClimate SystemsW3 credits2GR. Knutti, I. Medhaug
AbstractThis 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.
ObjectiveStudents 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 notesCopies of the slides are provided in electronic form.
LiteratureA 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 / NoticeTeaching: Reto Knutti, several keynotes to special topics by other professors
Course taught in german, slides in english
701-1216-00LNumerical Modelling of Weather and Climate Information W4 credits3GC. Schär, U. Lohmann
AbstractThe guiding principle of this lecture is that students can understand how weather and climate models are formulated from the governing physical principles and how they are used for climate and weather prediction purposes.
ObjectiveThe guiding principle of this lecture is that students can understand how weather and climate models are formulated from the governing physical principles and how they are used for climate and weather prediction purposes.
ContentThe 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 notesSlides and lecture notes will be made available at
Link
LiteratureList of literature will be provided.
Prerequisites / NoticePrerequisites: 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
701-1226-00LInter-Annual Phenomena and Their Prediction Information W2 credits2GC. Appenzeller
AbstractThis 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.
ObjectiveStudents will acquire an understanding of the key atmosphere and ocean processes involved, will gain experience in analyzing and predicting short-term climate variability and learn how operational weather and climate services are organised and how scientific developments can improve these services.
ContentThe course covers the following topics:

Part 1:
- a brief introduction into short-term climate variability and some basic concepts
- a brief review of climate data and the statistical concepts used for analysing climate 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 short-term climate variability (statistical methods, ensemble prediction systems. weekly to seasonal forecasts)
- verification methods for probabilistic forecast systems

Part 4:
- challenges for operational weather and climate services
- weather and climate extremes
- early warning systems
- a visit to the forecasting centre of MeteoSwiss
Lecture notesA pdf version of the slides will be available at
Link
LiteratureReferences are given during the lecture.
701-1252-00LClimate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate AdaptationW3 credits2V + 1UD. N. Bresch, R. Knutti
AbstractThe course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation.
ObjectiveStudents 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 (MATLAB), hence basic understanding of scientific programming forms a prerequisite of the course.
ContentThe 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
Lecture notesPowerpoint slides will be made available
Literature-
Prerequisites / NoticeHands-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)
851-0252-06LIntroduction to Social Networks: Theory, Methods and Applications Restricted registration - show details
Number of participants limited to 40.

This course is intended for students interested in data analysis and with basic knowledge of inferential statistics.
W3 credits2GC. Stadtfeld, A. Vörös
AbstractHumans 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.
ObjectiveThe 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)