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
NumberTitleTypeECTSHoursLecturers
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
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. Previous experience with PYTHON is useful but not required.
Biology
NumberTitleTypeECTSHoursLecturers
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 Link.
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 Link.
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 Link.
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
NumberTitleTypeECTSHoursLecturers
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: Link
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
Economics
NumberTitleTypeECTSHoursLecturers
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.
Finance
NumberTitleTypeECTSHoursLecturers
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:
Link
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: Link
LiteratureSee: Link
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.
Image Processing and Computer Vision
NumberTitleTypeECTSHoursLecturers
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.

Preferred:
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: Link.
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: Link
Information and Communication Technology
NumberTitleTypeECTSHoursLecturers
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.
227-0427-10LAdvanced Signal Analysis, Modeling, and Machine Learning Information W6 credits4GH.‑A. Loeliger
AbstractThe course develops a selection of topics pivoting around graphical models (factor graphs), state space methods, sparsity, and pertinent algorithms.
ObjectiveThe course develops a selection of topics pivoting around factor graphs, state space methods, and pertinent algorithms:
- factor graphs and message passing algorithms
- hidden-​Markov models
- linear state space models, Kalman filtering, and recursive least squares
- Gaussian message passing
- Gibbs sampling, particle filter
- recursive local polynomial fitting & applications
- parameter learning by expectation maximization
- sparsity and spikes
- binary control and digital-​to-analog conversion
- duality and factor graph transforms
Lecture notesLecture notes
Prerequisites / NoticeSolid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning".
Material Modelling and Simulation
NumberTitleTypeECTSHoursLecturers
327-2201-00LTransport Phenomena IIW5 credits4GJ. Vermant
AbstractNumerical and analytical methods for real-world "Transport Phenomena"; atomistic understanding of transport properties based on kinetic theory and mesoscopic models; fundamentals, applications, and simulations
ObjectiveThe teaching goals of this course are on five different levels:
(1) Deep understanding of fundamentals: kinetic theory, mesoscopic models, ...
(2) Ability to use the fundamental concepts in applications
(3) Insight into the role of boundary conditions
(4) Knowledge of a number of applications
(5) Flavor of numerical techniques: finite elements, lattice Boltzmann, ...
ContentThermodynamics of Interfaces
Interfacial Balance Equations
Interfacial Force-Flux Relations
Polymer Processing
Transport Around a Sphere
Refreshing Topics in Equilibrium Statistical Mechanics
Kinetic Theory of Gases
Kinetic Theory of Polymeric Liquids
Transport in Biological Systems
Dynamic Light Scattering
Lecture notesThe course is based on the book D. C. Venerus and H. C. Öttinger, A Modern Course in Transport Phenomena (Cambridge University Press, 2018)
Literature1. D. C. Venerus and H. C. Öttinger, A Modern Course in Transport Phenomena (Cambridge University Press, 2018)
2. R. B. Bird, W. E. Stewart, and E. N. Lightfoot, Transport Phenomena, 2nd Ed. (Wiley, 2001)
3. Deen,W. Analysis of Transport Phenomena, Oxford University Press, 2012
4. R. B. Bird, Five Decades of Transport Phenomena (Review Article), AIChE J. 50 (2004) 273-287
Prerequisites / NoticeComplex numbers. Vector analysis (integrability; Gauss' divergence theorem). Laplace and Fourier transforms. Ordinary differential equations (basic ideas). Linear algebra (matrices; functions of matrices; eigenvectors and eigenvalues; eigenfunctions). Probability theory (Gaussian distributions; Poisson distributions; averages; moments; variances; random variables). Numerical mathematics (integration). Statistical thermodynamics (Gibbs' fundamental equation; thermodynamic potentials; Legendre transforms; Gibbs' phase rule; ergodicity; partition functions; Einstein's fluctuation theory). Linear irreversible thermodynamics (forces and fluxes; Fourier's, Newton's and Fick's laws for fluxes). Hydrodynamics (local equilibrium; balance equations for mass, momentum, energy and entropy). Programming and simulation techniques (Matlab, Monte Carlo simulations).
151-0515-00LContinuum Mechanics 2W4 credits2V + 1UE. Mazza, R. Hopf
AbstractAn introduction to finite deformation continuum mechanics and nonlinear material behavior. Coverage of basic tensor- manipulations and calculus, descriptions of kinematics, and balance laws . Discussion of invariance principles and mechanical response functions for elastic materials.
ObjectiveTo provide a modern introduction to the foundations of continuum mechanics and prepare students for further studies in solid
mechanics and related disciplines.
Content1. Tensors: algebra, linear operators
2. Tensors: calculus
3. Kinematics: motion, gradient, polar decomposition
4. Kinematics: strain
5. Kinematics: rates
6. Global Balance: mass, momentum
7. Stress: Cauchy's theorem
8. Stress: alternative measures
9. Invariance: observer
10. Material Response: elasticity
Lecture notesNone.
LiteratureRecommended texts:
(1) Nonlinear solid mechanics, G.A. Holzapfel (2000).
(2) An introduction to continuum mechanics, M.B. Rubin (2003).
Quantum Chemistry
NumberTitleTypeECTSHoursLecturers
529-0474-00LQuantum ChemistryW6 credits3GM. Reiher, T. Weymuth
AbstractIntroduction into the basic concepts of electronic structure theory and into numerical methods of quantum chemistry. Exercise classes are designed to deepen the theory; practical case studies using quantum chemical software to provide a 'hands-on' expertise in applying these methods.
ObjectiveNowadays, chemical research can be carried out in silico, an intellectual achievement for which Pople and Kohn have been awarded the Nobel prize of the year 1998. This lecture shows how that has been accomplished. It works out the many-particle theory of many-electron systems (atoms and molecules) and discusses its implementation into computer programs. A complete picture of quantum chemistry shall be provided that will allow students to carry out such calculations on molecules (for accompanying experimental work in the wet lab or as a basis for further study of the theory).
ContentBasic concepts of many-particle quantum mechanics. Derivation of the many-electron theory for atoms and molecules; starting with the harmonic approximation for the nuclear problem and with Hartree-Fock theory for the electronic problem to Moeller-Plesset perturbation theory and configuration interaction and to coupled cluster and multi-configurational approaches. Density functional theory. Case studies using quantum mechanical software.
Lecture notesHand-outs in German will be provided for each lecture (they are supplemented by (computer) examples that continuously illustrate how the theory works).

All information regarding this course, including links to the online streaming, will be available on this web page:
Link
LiteratureTextbooks on Quantum Chemistry:
F.L. Pilar, Elementary Quantum Chemistry, Dover Publications
I.N. Levine, Quantum Chemistry, Prentice Hall

Hartree-Fock in basis set representation:
A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, McGraw-Hill

Textbooks on Computational Chemistry:
F. Jensen, Introduction to Computational Chemistry, John Wiley & Sons
C.J. Cramer, Essentials of Computational Chemistry, John Wiley & Sons
Prerequisites / NoticeBasic knowledge in quantum mechanics (e.g. through course physical chemistry III - quantum mechanics) required
Simulation of Semiconductor Devices
Simulation of Semiconductor Devices (not eligible for credits)
NumberTitleTypeECTSHoursLecturers
227-0056-00LSemiconductor Devices Information E-4 credits2V + 2UC. Bolognesi
AbstractThe course covers the basic principles of semiconductor devices in micro-, opto-, and power electronics. It imparts knowledge both of the basic physics and on the operation principles of pn-junctions, diodes, contacts, bipolar transistors, MOS devices, solar cells, photodetectors, LEDs and laser diodes.
ObjectiveUnderstanding of the basic principles of semiconductor devices in micro-, opto-, and power electronics.
ContentBrief survey of the history of microelectronics. Basic physics: Crystal structure of solids, properties of silicon and other semiconductors, principles of quantum mechanics, band model, conductivity, dispersion relation, equilibrium statistics, transport equations, generation-recombination (G-R), Quasi-Fermi levels. Physical and electrical properties of the pn-junction. pn-diode: Characteristics, small-signal behaviour, G-R currents, ideality factor, junction breakdown. Contacts: Schottky contact, rectifying barrier, Ohmic contact, Heterojunctions. Bipolar transistor: Operation principles, modes of operation, characteristics, models, simulation. MOS devices: Band diagram, MOSFET operation, CV- and IV characteristics, frequency limitations and non-ideal behaviour. Optoelectronic devices: Optical absorption, solar cells, photodetector, LED, laser diode.
Lecture notesLecture slides.
LiteratureThe lecture course follows the book Neamen, Semiconductor Physics and Devices, ISBN 978-007-108902-9, Fr. 89.00
Prerequisites / NoticeQualifications: Physics I+II
Systems Design
NumberTitleTypeECTSHoursLecturers
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
363-0588-00LComplex Networks Information W4 credits2V + 1UF. Schweitzer
AbstractThe course provides an overview of the methods and abstractions used in (i) the quantitative study of complex networks, (ii) empirical network analysis, (iii) the study of dynamical processes in networked systems, (iv) the analysis of robustness of networked systems, (v) the study of network evolution, and (vi) data mining techniques for networked data sets.
Objective* the network approach to complex systems, where actors are represented as nodes and interactions are represented as links
* learn about structural properties of classes of networks
* learn about feedback mechanism in the formation of networks
* learn about statistical inference and data mining techniques for data on networked systems
* learn methods and abstractions used in the growing literature on complex networks
ContentNetworks matter! This holds for social and economic systems, for technical infrastructures as well as for information systems. Increasingly, these networked systems are outside the control of a centralized authority but rather evolve in a distributed and self-organized way. How can we understand their evolution and what are the local processes that shape their global features? How does their topology influence dynamical processes like diffusion? And how can we characterize the importance of specific nodes?

This course provides a systematic answer to such questions, by developing methods and tools which can be applied to networks in diverse areas like infrastructure, communication, information systems, biology or (online) social networks. In a network approach, agents in such systems (like e.g. humans, computers, documents, power plants, biological or financial entities) are represented as nodes, whereas their interactions are represented as links.

The first part of the course, "Introduction to networks: basic and advanced metrics", describes how networks can be represented mathematically and how the properties of their link structures can be quantified empirically.

In a second part "Stochastic Models of Complex Networks" we address how analytical statements about crucial properties like connectedness or robustness can be made based on simple macroscopic stochastic models without knowing the details of a topology.

In the third part we address "Dynamical processes on complex networks". We show how a simple model for a random walk in networks can give insights into the authority of nodes, the efficiency of diffusion processes as well as the existence of community structures.

A fourth part "Network Optimisation and Inference" introduces models for the emergence of complex topological features which are due to stochastic optimization processes, as well as statistical methods to detect patterns in large data sets on networks.

In a fifth part, we address "Network Dynamics", introducing models for the emergence of complex features that are due to (i) feedback phenomena in simple network growth processes or (iii) order correlations in systems with highly dynamic links.

A final part "Research Trends" introduces recent research on the application of data mining and machine learning techniques to relational data.
Lecture notesThe lecture slides are provided as handouts - including notes and literature sources - to registered students only.
All material is to be found on Moodle at the following URL: Link
LiteratureSee handouts. Specific literature is provided for download - for registered students, only.
Prerequisites / NoticeThere are no pre-requisites for this course. Self-study tasks (to be solved analytically and by means of computer simulations) are provided as home work. Weekly exercises (45 min) are used to discuss selected solutions. Active participation in the exercises is strongly suggested for a successful completion of the final exam.
363-0543-00LAgent-Based Modelling of Social Systems Information W3 credits2V + 1UF. Schweitzer, G. Vaccario
AbstractAgent-based modeling is introduced as a bottom-up approach to understand the complex dynamics of social systems. The course is based on formal models of agents and their interactions. Computer simulations using Python allow the quantitative analysis of a wide range of social phenomena, e.g. cooperation and competition, opinion dynamics, spatial interactions and behaviour in social networks.
ObjectiveA successful participant of this course is able to
- understand the rationale of agent-based models of social systems
- understand the relation between rules implemented at the individual level and the emerging behavior at the global level
- learn to choose appropriate model classes to characterize different social systems
- grasp the influence of agent heterogeneity on the model output
- efficiently implement agent-based models using Python and visualize the output
ContentThis full-featured course on agent-based modeling (ABM) allows participants with no prior expertise to understand concepts, methods and tools of ABM, to apply them in their master or doctoral thesis. We focus on a formal description of agents and their interactions, to allow for a suitable implementation in computer simulations. Given certain rules for the agents, we are interested to model their collective dynamics on the systemic level.

Agent-based modeling is introduced as a bottom-up approach to understand the complex dynamics of social systems.
Agents represent the basic constituents of such systems. The are described by internal states or degrees of freedom (opinions, strategies, etc.), the ability to perceive and change their environment, and the ability to interact with other agents. Their individual (microscopic) actions and interactions with other agents, result in macroscopic (collective, system) dynamics with emergent properties, which we want to understand and to analyze.

The course is structured in three main parts. The first two parts introduce two main agent concepts - Boolean agents and Brownian agents, which differ in how the internal dynamics of agents is represented. Boolean agents are characterized by binary internal states, e.g. yes/no opinion, while Brownian agents can have a continuous spectrum of internal states, e.g. preferences and attitudes. The last part introduces models in which agents interact in physical space, e.g. migrate or move collectively.

Throughout the course, we will discuss a wide variety of application areas, such as:
- opinion dynamics and social influence,
- cooperation and competition,
- online social networks,
- systemic risk
- emotional influence and communication
- swarming behavior
- spatial competition

While the lectures focus on the theoretical foundations of agent-based modeling, weekly exercise classes provide practical skills. Using the Python programming language, the participants implement agent-based models in guided and in self-chosen projects, which they present and jointly discuss.
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 background in mathematics and an interest in formal modeling and in computer simulations, and should be motivated to learn about social systems from a quantitative perspective.

Prior knowledge of Python is not necessary.

Self-study tasks are provided as home work for small teams (2-4 members).
Weekly exercises (45 min) are used to discuss the solutions and guide the students.

The examination will account for 70% of the grade and will be conducted electronically. The "closed book" rule applies: no books, no summaries, no lecture materials. The exam questions and answers will be only in English. The use of a paper-based dictionary is permitted.
The group project to be handed in at the beginning of July will count 30% to the final grade.
Theoretical Physics
In the Master's programme in Applied Mathematics 402-0204-00L Electrodynamics is eligible as a course unit in the application area Theoretical Physics, but only if 402-0224-00L Theoretical Physics wasn't or isn't recognised for credits (neither in the Bachelor's nor in the Master's programme). For the category assignment take contact with the Study Administration Office (Link) after having received the credits.
NumberTitleTypeECTSHoursLecturers
402-0812-00LComputational Statistical Physics Information W8 credits2V + 2UM. Krstic Marinkovic
AbstractComputer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems.

Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization.
ObjectiveThe lecture will give a deeper insight into computer simulation methods in statistical physics. Thus, it is an ideal continuation of the lecture
"Introduction to Computational Physics" of the autumn semester. In the first part students learn to apply the following methods: Classical Monte Carlo-simulations, finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Moreover, students learn about the application of statistical physics methods to Boltzmann machines and how to simulate non-equilibrium systems.

In the second part, students apply molecular dynamics simulation methods. This part includes long range interactions, Ewald summation and discrete elements.
ContentComputer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization.
Lecture notesLecture notes and slides are available online and will be distributed if desired.
LiteratureLiterature recommendations and references are included in the lecture notes.
Prerequisites / NoticeSome basic knowledge about statistical physics, classical mechanics and computational methods is recommended.
402-0810-00LComputational Quantum Physics
Special Students UZH must book the module PHY522 directly at UZH.
W8 credits2V + 2UM. H. Fischer
AbstractThis course provides an introduction to simulation methods for quantum systems. Starting from the one-body problem, a special emphasis is on quantum many-body problems, where we cover both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, matrix product states, and quantum Monte Carlo methods).
ObjectiveThrough lectures and practical programming exercises, after this course:
Students are able to describe the difficulties of quantum mechanical simulations.
Students are able to explain the strengths and weaknesses of the methods covered.
Students are able to select an appropriate method for a given problem.
Students are able to implement basic versions of all algorithms discussed.
Lecture notesA script for this lecture will be provided.
LiteratureA list of additional references will be provided in the script.
Prerequisites / NoticeA basic knowledge of quantum mechanics, numerical tools (numerical differentiation and integration, linear solvers, eigensolvers, root solvers, optimization), and a programming language (for the teaching assignments, you are free to choose your preferred one).
402-0206-00LQuantum Mechanics II
Special Students UZH must book the module PHY351 directly at UZH.
W10 credits3V + 2UP. Jetzer
AbstractMany-body quantum physics rests on symmetry considerations that lead to two kinds of particles, fermions and bosons. Formal techniques include Hartree-Fock theory and second-quantization techniques, as well as quantum statistics with ensembles. Few- and many-body systems include atoms, molecules, the Fermi sea, elastic chains, radiation and its interaction with matter, and ideal quantum gases.
ObjectiveBasic command of few- and many-particle physics for fermions and bosons, including second quantisation and quantum statistical techniques. Understanding of elementary many-body systems such as atoms, molecules, the Fermi sea, electromagnetic radiation and its interaction with matter, ideal quantum gases and relativistic theories.
ContentThe description of indistinguishable particles leads us to (exchange-) symmetrized wave functions for fermions and bosons. We discuss simple few-body problems (Helium atoms, hydrogen molecule) und proceed with a systematic description of fermionic many body problems (Hartree-Fock approximation, screening, correlations with applications on atomes and the Fermi sea). The second quantisation formalism allows for the compact description of the Fermi gas, of elastic strings (phonons), and the radiation field (photons). We study the interaction of radiation and matter and the associated phenomena of radiative decay, light scattering, and the Lamb shift. Quantum statistical description of ideal Bose and Fermi gases at finite temperatures concludes the program. If time permits, we will touch upon of relativistic one particle physics, the Klein-Gordon equation for spin-0 bosons and the Dirac equation describing spin-1/2 fermions.
LiteratureG. Baym, Lectures on Quantum Mechanics (Benjamin, Menlo Park, California, 1969)
L.I. Schiff, Quantum Mechanics (Mc-Graw-Hill, New York, 1955)
A. Messiah, Quantum Mechanics I & II (North-Holland, Amsterdam, 1976)
E. Merzbacher, Quantum Mechanics (John Wiley, New York, 1998)
C. Cohen-Tannoudji, B. Diu, F. Laloe, Quantum Mechanics I & II (John Wiley, New York, 1977)
P.P. Feynman and A.R. Hibbs, Quantum Mechanics and Path Integrals (Mc Graw-Hill, New York, 1965)
A.L. Fetter and J.D. Walecka, Theoretical Mechanics of Particles and Continua (Mc Graw-Hill, New York, 1980)
J.J. Sakurai, Modern Quantum Mechanics (Addison Wesley, Reading, 1994)
J.J. Sakurai, Advanced Quantum mechanics (Addison Wesley)
F. Gross, Relativistic Quantum Mechanics and Field Theory (John Wiley, New York, 1993)
Prerequisites / NoticeBasic knowledge of single-particle Quantum Mechanics
402-0871-00LSolid State Theory
UZH students are not allowed to register this course unit at ETH. They must book the module PHY411 directly at UZH.
W10 credits4V + 1UV. Geshkenbein
AbstractThe course is addressed to students in experimental and theoretical condensed matter physics and provides a theoretical introduction to a variety of important concepts used in this field.
ObjectiveThe course provides a theoretical frame for the understanding of basic pinciples in solid state physics. Such a frame includes the topics of symmetries, band structures, many body interactions, Landau Fermi-liquid theory, and specific topics such as transport, Quantum Hall effect and magnetism. The exercises illustrate the various themes in the lecture and help to develop problem-solving skills. The student understands basic concepts in solid state physics and is able to solve simple problems. No diagrammatic tools will be used.
ContentThe course is addressed to students in experimental and theoretical condensed matter physics and provides a theoretical introduction to a variety of important concepts used in this field. The following subjects will be covered: Symmetries and their handling via group theoretical concepts, electronic structure in crystals, insulators-semiconductors-metals, phonons, interaction effects, (un-)screened Fermi-liquids, linear response theory, collective modes, screening, transport in semiconductors and metals, magnetism, Mott-insulators, quantum-Hall effect.
Lecture notesin English
402-0844-00LQuantum Field Theory II
UZH students are not allowed to register this course unit at ETH. They must book the corresponding module directly at UZH.
W10 credits3V + 2UN. Beisert
AbstractThe subject of the course is modern applications of quantum field theory with emphasis on the quantization of non-abelian gauge theories.
ObjectiveThe goal of this course is to lay down the path integral formulation of quantum field theories and in particular to provide a solid basis for the study of non-abelian gauge theories and of the Standard Model
ContentThe following topics will be covered:
- path integral quantization
- non-abelian gauge theories and their quantization
- systematics of renormalization, including BRST symmetries, Slavnov-Taylor Identities and the Callan-Symanzik equation
- the Goldstone theorem and the Higgs mechanism
- gauge theories with spontaneous symmetry breaking and their quantization
- renormalization of spontaneously broken gauge theories and quantum effective actions
LiteratureM.E. Peskin and D.V. Schroeder, "An introduction to Quantum Field Theory", Perseus (1995).
S. Pokorski, "Gauge Field Theories" (2nd Edition), Cambridge Univ. Press (2000)
P. Ramond, "Field Theory: A Modern Primer" (2nd Edition), Westview Press (1990)
S. Weinberg, "The Quantum Theory of Fields" (Volume 2), CUP (1996).
402-0394-00LTheoretical Cosmology
Special Students UZH must book the module AST513 directly at UZH.
W10 credits4V + 2UL. M. Mayer, J. Yoo
AbstractThis is the second of a two course series which starts with "General Relativity" and continues in the spring with "Theoretical Astrophysics and Cosmology", where the focus will be on applying general relativity to cosmology as well as developing the modern theory of structure formation in a cold dark matter Universe.
ObjectiveLearning the fundamentals of modern physical cosmology. This
entails understanding the physical principles behind the description
of the homogeneous Universe on large scales in the first part of the
course, and moving on to the inhomogeneous Universe model where
perturbation theory is used to study the development of structure
through gravitational instability in the second part of the course.
Modern notions of dark matter and dark energy will also be introduced and discussed.
ContentThe course will cover the following topics:
- Homogeneous cosmology
- Thermal history of the universe, recombination, baryogenesis and nucleosynthesis
- Dark matter and Dark Energy
- Inflation
- Perturbation theory: Relativistic and Newtonian
- Model of structure formation and initial conditions from Inflation
- Cosmic microwave background anisotropies
- Spherical collapse and galaxy formation
- Large scale structure and cosmological probes
Lecture notesIn 2021, the lectures will be live-streamed online at ETH from the Room HPV G5 at the lecture hours. The recordings will be available at the ETH website. The detailed information will be provided by the course website and the SLACK channel.
LiteratureSuggested textbooks:
H.Mo, F. Van den Bosch, S. White: Galaxy Formation and Evolution
S. Carroll: Space-Time and Geometry: An Introduction to General Relativity
S. Dodelson: Modern Cosmology
Secondary textbooks:
S. Weinberg: Gravitation and Cosmology
V. Mukhanov: Physical Foundations of Cosmology
E. W. Kolb and M. S. Turner: The Early Universe
N. Straumann: General relativity with applications to astrophysics
A. Liddle and D. Lyth: Cosmological Inflation and Large Scale Structure
Prerequisites / NoticeKnowledge of General Relativity is recommended.
» Electives Theoretical Physics
Transportation Science
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.