Search result: Catalogue data in Spring Semester 2021

Mathematics Master Information
Application Area
Only necessary and eligible for the Master degree in Applied Mathematics.
One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area.
Atmospherical Physics
701-1216-00LNumerical Modelling of Weather and Climate Information W4 credits3GC. Schär, J. Vergara Temprado, M. Wild
AbstractThe 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.
ObjectiveAt 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.
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
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. Previous experience with PYTHON is useful but not required.
551-0016-00LBiology II Information W2 credits2VM. Stoffel, E. Hafen, K. Köhler
AbstractThe lecture course Biology II, together with the course Biology I of the previous winter semester, is a basic introductory course into biology for students of materials sciences, of chemistry and of chemical engineering.
ObjectiveThe objective of the lecture course Biology II is the understanding of form, function, and development of animals and of the basic underlying mechanisms.
ContentThe following numbers of chapters refer to the text-book "Biology" (Campbell & Rees, 10th edition, 2015) on which the course is based.

Chapters 1-4 are a basic prerequisite. The sections "Structure of the Cell" (Chapters 5-10, 12, 17) and "General Genetics" (Chapters 13-16, 18, 46) are covered by the lecture Biology I.

1. Genomes, DNA Technology, Genetic Basis of Development

Chapter 19: Eukaryotic Genomes: Organization, Regulation, and Evolution
Chapter 20: DNA Technology and Genomics
Chapter 21: The Genetic Basis of Development

2. Form, Function, and Development of Animals I

Chapter 40: Basic Principles of Animal Form and Function
Chapter 41: Animal Nutrition
Chapter 44: Osmoregulation and Excretion
Chapter 47: Animal Development

3. Form, Function, and Develeopment of Animals II

Chapter 42: Circulation and Gas Exchange
Chapter 43: The Immune System
Chapter 45: Hormones and the Endocrine System
Chapter 48: Nervous Systems
Chapter 49: Sensory and Motor Mechanisms
Lecture notesThe course follows closely the recommended text-book. Additional handouts may be provided by the lecturers.
LiteratureThe following text-book is the basis for the courses Biology I and II:

Biology, Campbell and Rees, 10th Edition, 2015, Pearson/Benjamin Cummings, ISBN 978-3-8632-6725-4
Prerequisites / NoticePrerequisite: Lecture course Biology I of autumn semester
262-0200-00LBayesian Phylodynamics – Taming the BEASTW4 credits2G + 2AT. Stadler, T. Vaughan
AbstractHow fast is COVID-19 spreading at the moment? How fast was Ebola spreading in West Africa? Where and when did these epidemic outbreak start? How can we construct the phylogenetic tree of great apes, and did gene flow occur between different apes? At the end of the course, students will have designed, performed, presented, and discussed their own phylodynamic data analysis to answer such questions.
ObjectiveAttendees 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
ContentDuring the first part of the block course, the theoretical concepts of Bayesian phylodynamics will be presented by us as well as leading international researchers in that area. The presentations will be followed by attendees using the software package BEAST v2 to apply these theoretical concepts to empirical data. We will use previously published datasets on e.g. COVID-19, Ebola, Zika, Yellow Fever, Apes, and Penguins for analysis. Examples of these practical tutorials are available on
In the second part of the block course, 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. A final written report on the research project has to be submitted after the block course for grading.
Lecture notesAll material will be available on
LiteratureThe 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.
More detailed information is available on
Prerequisites / NoticeThis class builds upon the content which we teach in the Computational Biology class (636-0017-00L). Attendees must have either taken the Computational Biology class or acquired the content elsewhere.
Control and Automation
151-0660-00LModel Predictive Control Information W4 credits2V + 1UM. Zeilinger, A. Carron
AbstractModel predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.
ObjectiveDesign and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.
Content- Review of required optimal control theory
- Basics on optimization
- Receding-horizon control (MPC) for constrained linear systems
- Theoretical properties of MPC: Constraint satisfaction and stability
- Computation: Explicit and online MPC
- Practical issues: Tracking and offset-free control of constrained systems, soft constraints
- Robust MPC: Robust constraint satisfaction
- Nonlinear MPC: Theory and computation
- Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization
- Simulation-based project providing practical experience with MPC
Lecture notesScript / lecture notes will be provided.
Prerequisites / NoticeOne semester course on automatic control, Matlab, linear algebra.
Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control.

Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week).
227-0207-00LNonlinear Systems and Control Information
Prerequisite: Control Systems (227-0103-00L)
W6 credits4GE. Gallestey Alvarez, P. F. Al Hokayem
AbstractIntroduction to the area of nonlinear systems and their control. Familiarization with tools for analysis of nonlinear systems. Discussion of the various nonlinear controller design methods and their applicability to real life problems.
ObjectiveOn completion of the course, students understand the difference between linear and nonlinear systems, know the mathematical techniques for analysing these systems, and have learnt various methods for designing controllers accounting for their characteristics.

Course puts the student in the position to deploy nonlinear control techniques in real applications. Theory and exercises are combined for better understanding of the virtues and drawbacks present in the different methods.
ContentVirtually all practical control problems are of nonlinear nature. In some cases application of linear control methods leads to satisfactory controller performance. In many other cases however, only application of nonlinear analysis and control synthesis methods will guarantee achievement of the desired objectives.

During the past decades mature nonlinear controller design methods have been developed and have proven themselves in applications. After an introduction of the basic methods for analysing nonlinear systems, these methods will be introduced together with a critical discussion of their pros and cons. Along the course the students will be familiarized with the basic concepts of nonlinear control theory.

This course is designed as an introduction to the nonlinear control field and thus no prior knowledge of this area is required. The course builds, however, on a good knowledge of the basic concepts of linear control and mathematical analysis.
Lecture notesAn english manuscript will be made available on the course homepage during the course.
LiteratureH.K. Khalil: Nonlinear Systems, Prentice Hall, 2001.
Prerequisites / NoticePrerequisites: Linear Control Systems, or equivalent.
227-0224-00LStochastic Systems
Does not take place this semester.
W4 credits2V + 1Uto be announced
AbstractProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
ObjectiveStochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.
Content- Stochastic processes
- Stochastic calculus (Ito)
- Stochastic differential equations
- Discrete time stochastic difference equations
- Stochastic processes AR, MA, ARMA, ARMAX, GARCH
- Kalman filter
- Stochastic optimal control
- Applications in finance and engineering
Lecture notesH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
151-0530-00LNonlinear Dynamics and Chaos IIW4 credits4GG. Haller
AbstractThe internal structure of chaos; Hamiltonian dynamical systems; Normally hyperbolic invariant manifolds; Geometric singular perturbation theory; Finite-time dynamical systems
ObjectiveThe course introduces the student to advanced, comtemporary concepts of nonlinear dynamical systems analysis.
ContentI. The internal structure of chaos: symbolic dynamics, Bernoulli shift map, sub-shifts of finite type; chaos is numerical iterations.

II.Hamiltonian dynamical systems: conservation and recurrence, stability of fixed points, integrable systems, invariant tori, Liouville-Arnold-Jost Theorem, KAM theory.

III. Normally hyperbolic invariant manifolds: Crash course on differentiable manifolds, existence, persistence, and smoothness, applications.
IV. Geometric singular perturbation theory: slow manifolds and their stability, physical examples. V. Finite-time dynamical system; detecting Invariant manifolds and coherent structures in finite-time flows
Lecture notesStudents have to prepare their own lecture notes
LiteratureBooks will be recommended in class
Prerequisites / NoticeNonlinear Dynamics I (151-0532-00) or equivalent
151-0566-00LRecursive Estimation Information W4 credits2V + 1UR. D'Andrea
AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
Lecture notesLecture notes available on course website:
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
363-0552-00LEconomic Growth and Resource UseW3 credits2GC. Karydas
AbstractThe course deals with the factors that contribute to economic development. Throughout the course theoretical economic modelling will be used to discuss the effects of factors – such as land, human/physical capital, technology, fossil energy resources, and climate change – on economic growth and to draw conclusions for the future.
ObjectiveThe general objective of the course is to provide students tools and intuition to:

i) think in a structured way – though economic modelling – about the factors that have lead to the different growth experiences among countries, and still shape our contemporary situation;
ii) assess and design policies on the basis of economic development;
iii) draw conclusions for the future of economic development, that take into account prevalent issues such as the scarcity of fossil energy resources and climate change.
ContentWhy is economic growth worth studying? Which are the factors behind economic growth? What is the role of natural resources in shaping economic development? Is our finite planet able to support sustainable long-term economic growth? Economics aims at explaining human behaviour; how do we model it and how can we steer it for the better? How do you design an efficient economic policy for a sustainable future? What is sustainable anyway? These are some of the questions you will learn to answer in this course.

After spending the first lecture on overviewing the course, and the second lecture on building our mathematical and economic foundation, we begin with the three main modules that comprise this course.

The first module – called “Land and Economic Growth” – deals with the historical evolution of the factors behind economic development from the pre-industrial times to our modern growth experiences. By studying the history of economic growth, we understand change and how the society we live in came to be. In this module we will develop economic models that capture the transition from an era of miniscule economic growth that persisted for millennia before the industrial revolution – with land and human labour as the main inputs to economic activity, to our modern growth experience where the continuous improvement in technology and services is our status quo.

The second module – called “Non-Renewable Resources and Growth” – deals with the problem of optimal exploitation of non-renewable resources, as well as with the issue of “Resource Curse” – i.e., the observed negative relationship between economic development and resource abundance. Emerging in the 1970s due to two oil crises, the problem of the economy’s extreme dependence on fossil and depletable energy resources sparked a great deal of research to guide our way forward. Some important questions we will formally answer in this module are the following. How do we optimally exploit a given stock of a non-renewable resource? What affects the prices of non-renewable resources? If fossil energy sources – a (so far) important input to production – are getting ever depleted, is long-term growth possible? How do we explain the “Resource Curse” and what are the policies that allow a sustainable future in countries that suffer from such a curse?

The third module – called “Climate Change and Growth” – deals with the pressing problem of our changing climate. Greenhouse gas emissions – so far essential for economic activity – accumulate in the atmosphere and alter environmental patterns. This phenomenon – commonly known as climate change – is responsible for the increase in the frequency and the intensity of natural disasters, which damage our stocks of capital and put a drag on economic growth. To derive appropriate policies for a sustainable future, we will incorporate these aspects in workhorse models of the economics and finance literature. Students will learn how to derive and set the “correct” price on the use of polluting energy resources from the perspective of policy-makers. Additionally, pricing of climate change risks for financial markets is important, both for individual investors and central banks, as it is they who provide liquidity to firms to pursue their long-term growth targets. Accordingly, we will close the lecture with the pricing of climate change risks from an investor’s perspective.

After the last lecture of each of the three modules students will be handed out an exercise set which will be submitted by the beginning of the following week’s lecture. That lecture will be an exercise session where we will discuss the solutions in class. Each exercise set will be graded. The average grade from the best two exercise sets will account for 25% of the final grade; the rest 75% will be determined by a written exam.
Lecture notesLecture Notes of the course will be sent by email to officially subscribed students.
LiteratureThe main reference of the course is the set of lecture notes; students will also be encouraged to read some influential academic articles dealing with the issues under study.
Prerequisites / NoticeKnowledge of basic calculus (differentiation - integration) and basic statistics (e.g. what is an expectation; variance-covariance) is considered as a prerequisite. Elementary knowledge of dynamic systems analysis, optimal control theory and economic theory is a plus but not a prerequisite.
363-0514-00LEnergy Economics and Policy
It is recommended for students to have taken a course in introductory microeconomics. If not, they should be familiar with microeconomics as in, for example,"Microeconomics" by Mankiw & Taylor and the appendices 4 and 7 of the book "Microeconomics" by Pindyck & Rubinfeld.
W3 credits2GM. Filippini, S. Srinivasan
AbstractAn introduction to energy economics and policy that covers the following topics: energy demand, investment in energy efficiency, investment in renewables, energy markets, market failures and behavioral anomalies, market-based and non-market based energy and climate policy instruments in industrialized and developing countries.
ObjectiveThe students will develop an understanding of economic principles and tools necessary to analyze energy issues and to understand energy and climate policy instruments. Emphasis will be put on empirical analysis of energy demand and supply, market failures, behavioral anomalies, energy and climate policy instruments in industrialized and developing countries, and investments in renewables and in energy-efficient technologies.
ContentThe course provides an introduction to energy economics principles and policy applications. The first part of the course will introduce the microeconomic foundation of energy demand and supply as well as market failures and behavioral anomalies. In a second part, we introduce the concept of investment analysis (such as the NPV) in the context of renewable and energy-efficient technologies. In the last part, we use the previously introduced concepts to analyze energy policies: from a government perspective, we discuss the mechanisms and implications of market oriented and non-market oriented policy instruments as well as applications in developing countries.

Throughout the entire course, we combine the material with insights from current research in energy economics. This combination will enable students to understand standard scientific literature in the field of energy economics and policy. Moreover, the class aims to show students how to relate current issues in the energy and climate spheres that influence industrialized and developing countries to insights from energy economics and policy.

Course evaluation: at the end of the course, there will be a written exam covering the topics of the course.
Prerequisites / NoticeIt is recommended for students to have taken a course in introductory microeconomics. If not, they should be familiar with microeconomics as in, for example, "Microeconomics" by Mankiw & Taylor and the appendices 4 and 7 of the book "Microeconomics" by Pindyck & Rubinfeld.
364-0576-00LAdvanced Sustainability Economics Information
PhD course, open for MSc students
W3 credits3GL. Bretschger, A. Pattakou
AbstractThe course covers current resource and sustainability economics, including ethical foundations of sustainability, intertemporal optimisation in capital-resource economies, sustainable use of non-renewable and renewable resources, pollution dynamics, population growth, and sectoral heterogeneity. A final part is on empirical contributions, e.g. the resource curse, energy prices, and the EKC.
ObjectiveUnderstanding of the current issues and economic methods in sustainability research; ability to solve typical problems like the calculation of the growth rate under environmental restriction with the help of appropriate model equations.
363-0575-00LEconomic Growth, Cycles and PolicyW3 credits2GH. Gersbach
AbstractThis intermediate course focuses on the core thinking devices and foundations in macroeconomics and monetary economics, and uses these devices to understand economic growth, business cycles, crises as well as how to conduct monetary and fiscal policies and policies to foster the stability of financial and economic systems.
Objective- Fundamental knowledge about the drivers of economic growth in the short and long run, key macroeconomic variables and observed patterns in developed countries

- Comprehensive understanding of core macroeconomic frameworks and thinking devices
ContentThis intermediate course focuses on the core thinking devices and foundations in macroeconomics and monetary economics, and uses these devices to understand economic growth, business cycles, crises as well as how to conduct monetary and fiscal policies and policies to foster the stability of financial and economic systems. The course is structured in the following way:

Part I: Basics
- Introduction
- IS-LM Model in Closed Economy (Repetition)
- Schools of Thought
- Consumption and Investment
- The Solow Growth Model

Part II: Special Themes
- Money Holding, Inflation, and Monetary Policy
- Crises in Market Economies
- IS-LM Model and Open Economy
- Theories of exchange rate determination
- Technical Appendix
Lecture notesCopies of the slides will be made available.
LiteratureChapters in
Manfred Gärtner (2009), Macroeconomics, Third Edition, Prentice Hall.
and selected chapters in other books and/or papers
Prerequisites / NoticeIt is required that participants have attended the lecture "Principles of Macroeconomics" (363-0565-00L).
363-0515-00LDecisions and MarketsW3 credits2VA. Bommier
AbstractThis course provides an introduction to microeconomics. The course emphasizes the conceptual foundations of microeconomics and contains concrete examples of their application.
ObjectiveThe purpose of this course is to provide master students with an introduction to graduate-level microeconomics, particularly for students considering further graduate work in economics, business administration or management science. The course provides the fundamental concepts and tools for graduate courses in economics offered at ETH and UZH.

After completing this course:
- Students will be able to understand and use existing models to make predictions of consumer and firm behavior.
- Students understand the fundamental welfare theorems and will be able to analyze equilibria of markets with perfect and imperfect competition.
- Students will be able to analyze under which conditions market allocations are not efficient (market failure).
ContentMicroeconomics is the branch of economics which studies the decision-making by an individual, household, firm, industry or level of government. The economic equilibrium is the result of agents' interactions. Microeconomics is an element of nearly every subfield in economic analysis today. This course introduces the fundamental frameworks which form the basis of many economic models.

Theory of the consumer:
- Consumer preferences and utility
- Budget sets and optimal choice
- Demand functions
- Labor supply and intertemporal choice
- Welfare economics

Theory of the producer:
- Technological constraints and the production function
- Cost minimization
- Profit maximization

Market structure:
- Perfectly competitive markets
- Monopoly behavior
- Duopoly behavior

General equilibrium analysis:
- Market equilibrium in an exchange economy
Lecture notesThe lecture will be based on lecture slides, which will be made available on Moodle.
LiteratureThe course is mostly based on the textbook by R. Serrano and A. Feldman: "A Short Course in Intermediate Microeconomics with Calculus" (Cambridge University Press, 2013). Another textbook of interest is "Intermediate Microeconomics: A Modern Approach" by H. Varian (Norton, 2014).
Exercises are available in the textbook by R. Serrano and A. Feldman ("A Short Course in Intermediate Microeconomics with Calculus", Cambridge University Press, 2013). More exercises can be found in the book "Workouts in Intermediate Microeconomics" by T. Bergstrom and H. Varian (Norton, 2010).
Prerequisites / NoticeThe course is open to students who have completed an undergraduate course in economics principles and an undergraduate course in multivariate calculus.
363-1017-00LRisk and Insurance EconomicsW3 credits2GI. Gemmo
AbstractThe course covers the economics of risk and insurance, in particular the following topics will be discussed:
2) individual decision making under risk
3) fundamentals of insurance
4) information asymmetries in insurance markets
5) the macroeconomic role of insurers
ObjectiveThe goal is to introduce students to basic concepts of risk, risk management and economics of insurance.
Content“The ability to define what may happen in the future and to choose among alternatives lies at the heart of contemporary societies. Risk management guides us over a vast range of decision-making from allocation of wealth to safeguarding public health, from waging war to planning a family, from paying insurance premiums to wearing a seatbelt, from planting corn to marketing cornflakes.” (Peter L. Bernstein)

Every member of society faces various decisions under uncertainty on a daily basis. Many individuals apply measures to manage these risks without even thinking about it; many are subject to behavioral biases when making these decisions. In the first part of this lecture, we discuss normative decision concepts, such as Expected Utility Theory, and contrast them with empirically observed behavior.

Students learn about the rationale for individuals to purchase insurance as part of a risk management strategy. In a theoretical framework, we then derive the optimal level of insurance demand and discuss how this result depends on the underlying assumptions. After learning the basics for understanding the specifications, particularities, and mechanisms of insurance markets, we discuss the consequences of information asymmetries in these markets.

Insurance companies do not only provide individuals with a way to decrease uncertainty of wealth, they also play a vital role for businesses that want to manage business risk, for the real economy by providing funds and pooling risks, and for the financial market by being important counterparties in numerous financial transactions. In the last part of this lecture, we shed light on these different roles of insurance companies. We compare the implications for different stakeholders and (insurance) markets in general.

Finally, course participants familiarize themselves with selected research papers that analyze individuals’ decision-making under risk or examine specific details about the different roles of insurance companies.
LiteratureMain literature:

- Eeckhoudt, L., Gollier, C., & Schlesinger, H. (2005). Economic and Financial Decisions under Risk. Princeton University Press.
- Zweifel, P., & Eisen, R. (2012). Insurance Economics. Springer.

Further readings:

- Dionne, G. (Ed.). (2013). Handbook of Insurance (2nd ed.). Springer.
- Hufeld, F., Koijen, R. S., & Thimann, C. (Eds.). (2017). The Economics, Regulation, and Systemic Risk of Insurance Markets. Oxford University Press.
- Niehaus, H., & Harrington, S. (2003). Risk Management and Insurance (2nd ed.). McGraw Hill.
- Rees, R., & Wambach, A. (2008). The Microeconomics of Insurance, Foundations and Trends® in Microeconomics, 4(1–2), 1-163.
401-8916-00LAdvanced Corporate Finance II (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: MFOEC144

Mind the enrolment deadlines at UZH:
W3 credits2VUniversity lecturers
AbstractTo provide the students with good understanding of the problems and issues in corporate finance.
ObjectiveTo provide the students with good understanding of the problems and issues in corporate finance.
ContentThe following topics are covered in this course: the role of information and incentives in determining the forms of financing a firm chooses; hedging; venture capital; initial public offerings; investment in very large projects; the setting up of a "bad" bank; the securitisation of commercial and industrial loans; the transfer of catastrophe risk to financial markets; agency in insurance; and dealing with a run on an insurance company.
Lecture notesSee:
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:
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.
Image Processing and Computer Vision
102-0617-01LMethodologies for Image Processing of Remote Sensing DataW3 credits2GI. Hajnsek, O. Frey, S. Li
AbstractThe aim of this course is to get an overview of several methodologies/algorithms for analysis of different sensor specific information products. It is focused at students that like to deepen their knowledge and understanding of remote sensing for environmental applications.
ObjectiveThe course is divided into two main parts, starting with a brief introduction to remote sensing imaging (4 lectures), and is followed by an introduction to different methodologies (8 lectures) for the quantitative estimation of bio-/geo-physical parameters. The main idea is to deepen the knowledge in remote sensing tools in order to be able to understand the information products, with respect to quality and accuracy.
ContentEach lecture will be composed of two parts:
Theory: During the first hour, we go trough the main concepts needed to understand the specific algorithm.
Practice: During the second hour, the student will test/develop the actual algorithm over some real datasets using Matlab. The student will not be asked to write all the code from scratch (especially during the first lectures), but we will provide some script with missing parts or pseudo-code. However, in the later lectures the student is supposed to build up some working libraries.
Lecture notesHandouts for each topic will be provided.
LiteratureSuggested readings:
T. M. Lillesand, R.W. Kiefer, J.W. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons Verlag, 2008
J. R. Jensen, Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall Series in Geograpic Information Science, 2000
227-0391-00LMedical Image Analysis
Basic knowledge of computer vision would be helpful.
W3 credits2GE. Konukoglu, M. A. Reyes Aguirre
AbstractIt 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.
ObjectiveThis lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas.
Prerequisites / NoticePrerequisites:
Basic concepts of mathematical analysis and linear algebra.

Basic knowledge of computer vision and machine learning would be helpful.

The course will be held in English.
227-0396-00LEXCITE Interdisciplinary Summer School on Bio-Medical Imaging Restricted registration - show details
The school admits 60 MSc or PhD students with backgrounds in biology, chemistry, mathematics, physics, computer science or engineering based on a selection process.

Students have to apply for acceptance. To apply a curriculum vitae and an application letter need to be submitted.
Further information can be found at:
W4 credits6GS. Kozerke, G. Csúcs, J. Klohs-Füchtemeier, S. F. Noerrelykke, M. P. Wolf
AbstractTwo-week summer school organized by EXCITE (Center for EXperimental & Clinical Imaging TEchnologies Zurich) on biological and medical imaging. The course covers X-ray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, optoacoustic imaging, infrared and optical microscopy, electron microscopy, image processing and analysis.
ObjectiveStudents understand basic concepts and implementations of biological and medical imaging. Based on relative advantages and limitations of each method they can identify preferred procedures and applications. Common foundations and conceptual differences of the methods can be explained.
ContentTwo-week summer school on biological and medical imaging. The course covers concepts and implementations of X-ray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, optoacoustic imaging, infrared and optical microscopy and electron microscopy. Multi-modal and multi-scale imaging and supporting technologies such as image analysis and modeling are discussed. Dedicated modules for physical and life scientists taking into account the various backgrounds are offered.
Lecture notesPresentation slides, Web links
Prerequisites / NoticeThe school admits 60 MSc or PhD students with backgrounds in biology, chemistry, mathematics, physics, computer science or engineering based on a selection process. To apply a curriculum vitae, a statement of purpose and applicants references need to be submitted. Further information can be found at:
Information and Communication Technology
227-0420-00LInformation Theory II Information W6 credits4GA. Lapidoth, S. M. Moser
AbstractThis course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory.
ObjectiveThe course's objective is to introduce the students to additional information measures and to equip them with the tools that are needed to conduct research in Information Theory as it relates to Communication Networks and to Statistics.
ContentSanov's Theorem, Rényi entropy and guessing, differential entropy, maximum entropy, the Gaussian channel, the entropy-power inequality, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, the Gelfand-Pinsker problem, and Fisher information.
Lecture notesn/a
LiteratureT.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006
Prerequisites / NoticeBasic introductory course on Information Theory.
  •  Page  1  of  2 Next page Last page     All