# Suchergebnis: Katalogdaten im Herbstsemester 2021

Mathematik Master | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Anwendungsgebiet Nur für das Master-Diplom in Angewandter Mathematik erforderlich und anrechenbar. In der Kategorie Anwendungsgebiet für den Master in Angewandter Mathematik muss eines der zur Auswahl stehenden Anwendungsgebiete gewählt werden. Im gewählten Anwendungsgebiet müssen mindestens 8 KP erworben werden. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Atmospherical Physics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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701-1221-00L | Dynamics of Large-Scale Atmospheric Flow | W | 4 KP | 2V + 1U | H. Wernli, L. Papritz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Die Vorlesung vermittelt die Grundlagen der Dynamik von aussertropischen Wettersystemen (quasi-geostrophische Dynamik, potentielle Vorticity, Rossby-Wellen, barokline Instabilität). Grundlegende Konzepte werden formal eingeführt, quantitativ angewendet und mit realen Beispielen illustriert und vertieft. Übungen (quantitativ und qualitativ) sind ein wesentlicher Bestandteil des Kurses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Verständnis für dynamische Prozesse in der Atmosphäre sowie deren mathematisch-physikalische Formulierung. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Die Atmosphärenphysik II behandelt vor allem die dynamischen Prozesse in der Erdatmosphäre. Diskutiert werden die Bewegungsgesetze der Atmosphäre und die Dynamik und Wechselwirkungen von synoptischen Systemen - also den wetterbestimmenden Hoch- und Tiefdruckgebieten. Mathematische Grundlage hierfuer ist insbesondere die Theorie der quasi-geostrophischen Bewegung, die im Rahmen der Vorlesung hergeleitet und interpretiert wird. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | Dynamics of large-scale atmospheric flow | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | - Holton J.R., An introduction to Dynamic Meteorogy. Academic Press, fourth edition 2004, - Pichler H., Dynamik der Atmosphäre, Bibliographisches Institut, 456 pp. 1997 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Voraussetzungen: Physik I, II, Umwelt Fluiddynamik | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Biology | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

636-0017-00L | Computational Biology | W | 6 KP | 3G + 2A | T. Vaughan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | The aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Attendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are: * stochastic models in molecular evolution * phylogenetic & phylodynamic inference * maximum likelihood and Bayesian statistics Attendees will apply these concepts to a number of applications yielding biological insight into: * epidemiology * pathogen evolution * macroevolution of species | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | The course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

Literatur | The course is not based on any of the textbooks below, but they are excellent choices as accompanying material: * Yang, Z. 2006. Computational Molecular Evolution. * Felsenstein, J. 2004. Inferring Phylogenies. * Semple, C. & Steel, M. 2003. Phylogenetics. * Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Basic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills. However, in case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place at D-BSSE from Wednesday, September 12 to Friday, September 14, i.e. BEFORE the official semester starting date Link For the Zurich-based students without R experience, we recommend the R course Link, or working through the script provided as part of this R course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

636-0007-00L | Computational Systems Biology | W | 6 KP | 3V + 2U | J. Stelling | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Study of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Biology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | U. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006. Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2010. B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

636-0009-00L | Evolutionary Dynamics | W | 6 KP | 2V + 1U + 2A | N. Beerenwinkel | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Evolutionary dynamics is concerned with the mathematical principles according to which life has evolved. This course offers an introduction to mathematical modeling of evolution, including deterministic and stochastic models, with an emphasis on tumor evolution. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | The goal of this course is to understand and to appreciate mathematical models and computational methods that provide insight into the evolutionary process in general and tumor evolution in particular. Students should analyze and evaluate models and their application critically and be able to design new models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Evolution is the one theory that encompasses all of biology. It provides a single, unifying concept to understand the living systems that we observe today. We will introduce several types of mathematical models of evolution to describe gene frequency changes over time in the context of different biological systems, focusing on asexual populations. Viruses and cancer cells provide the most prominent examples of such systems and they are at the same time of great biomedical interest. The course will cover some classical mathematical population genetics and population dynamics, and also introduce several new approaches. This is reflected in a diverse set of mathematical concepts which make their appearance throughout the course, all of which are introduced from scratch. Topics covered include the quasispecies equation, evolution of HIV, evolutionary game theory, evolutionary stability, evolutionary graph theory, tumor evolution, stochastic tunneling, genetic progression of cancer, diffusion theory, fitness landscapes, branching processes, and evolutionary escape. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | No. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | - Evolutionary Dynamics. Martin A. Nowak. The Belknap Press of Harvard University Press, 2006. - Evolutionary Theory: Mathematical and Conceptual Foundations. Sean H. Rice. Sinauer Associates, Inc., 2004. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Prerequisites: Basic mathematics (linear algebra, calculus, probability) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kompetenzen |
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Control and Automation | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

151-0563-01L | Dynamic Programming and Optimal Control | W | 4 KP | 2V + 1U | R. D'Andrea | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Introduction to Dynamic Programming and Optimal Control. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Covers the fundamental concepts of Dynamic Programming & Optimal Control. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Economics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

401-3929-00L | Financial Risk Management in Social and Pension Insurance | W | 4 KP | 2V | P. Blum | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Investment returns are an important source of funding for social and pension insurance, and financial risk is an important threat to stability. We study short-term and long-term financial risk and its interplay with other risk factors, and we develop methods for the measurement and management of financial risk and return in an asset/liability context with the goal of assuring sustainable funding. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Understand the basic asset-liability framework: essential principles and properties of social and pension insurance; cash flow matching, duration matching, valuation portfolio and loose coupling; the notion of financial risk; long-term vs. short-term risk; coherent measures of risk. Understand the conditions for sustainable funding: derivation of required returns; interplay between return levels, contribution levels and other parameters; influence of guaranteed benefits. Understand the notion of risk-taking capability: capital process as a random walk; measures of long-term risk and relation to capital; short-term solvency vs. long-term stability; effect of embedded options and guarantees; interplay between required return and risk-taking capability. Be able to study empirical properties of financial assets: the Normal hypothesis and the deviations from it; statistical tools for investigating relevant risk and return properties of financial assets; time aggregation properties; be able to conduct analysis of real data for the most important asset classes. Understand and be able to carry out portfolio construction: the concept of diversification; limitations to diversification; correlation breakdown; incorporation of constraints; sensitivities and shortcomings of optimized portfolios. Understand and interpret the asset-liability interplay: the optimized portfolio in the asset-liability framework; short-term risk vs. long-term risk; the influence of constraints; feasible and non-feasible solutions; practical considerations. Understand and be able to address essential problems in asset / liability management, e.g. optimal risk / return positioning, optimal discount rate, target value for funding ratio or turnaround issues. Have an overall view: see the big picture of what asset returns can and cannot contribute to social security; be aware of the most relevant outcomes; know the role of the actuary in the financial risk management process. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | For pension insurance and other forms of social insurance, investment returns are an important source of funding. In order to earn these returns, substantial financial risks must be taken, and these risks represent an important threat to financial stability, in the long term and in the short term. Risk and return of financial assets cannot be separated from one another and, hence, asset management and risk management cannot be separated either. Managing financial risk in social and pension insurance is, therefore, the task of reconciling the contradictory dimensions of 1. Required return for a sustainable funding of the institution, 2. Risk-taking capability of the institution, 3. Returns available from financial assets in the market, 4. Risks incurred by investing in these assets. This task must be accomplished under a number of constraints. Financial risk management in social insurance also means reconciling the long time horizon of the promised insurance benefits with the short time horizon of financial markets and financial risk. It is not the goal of this lecture to provide the students with any cookbook recipes that can readily be applied without further reflection. The goal is rather to enable the students to develop their own understanding of the problems and possible solutions associated with the management of financial risks in social and pension insurance. To this end, a rigorous intellectual framework will be developed and a powerful set of mathematical tools from the fields of actuarial mathematics and quantitative risk management will be applied. When analyzing the properties of financial assets, an empirical viewpoint will be taken using statistical tools and considering real-world data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | Extensive handouts will be provided. Moreover, practical examples and data sets in Excel and R will be made available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Solid base knowledge of probability and statistics is indispensable. Specialized concepts from financial and insurance mathematics as well as quantitative risk management will be introduced in the lecture as needed, but some prior knowledge in some of these areas would be an advantage. This course counts towards the diploma of "Aktuar SAV". The exams ONLY take place during the official ETH examination period. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

363-0537-00L | Resource and Environmental Economics | W | 3 KP | 2G | L. Bretschger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Relationship between economy and environment, market failures, external effects and public goods, contingent valuation, internalisation of externalities, economics of non-renewable resources, economics of renewable resources, environmental cost-benefit analysis, sustainability economics, and international resource and environmental problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | A successful completion of the course will enable a thorough understanding of the basic questions and methods of resource and environmental economics and the ability to solve typical problems using appropriate tools consisting of concise verbal explanations, diagrams or mathematical expressions. Concrete goals are first of all the acquisition of knowledge about the main questions of resource and environmental economics and about the foundation of the theory with different normative concepts in terms of efficiency and fairness. Secondly, students should be able to deal with environmental externalities and internalisation through appropriate policies or private negotiations, including knowledge of the available policy instruments and their relative strengths and weaknesses. Thirdly, the course will allow for in-depth economic analysis of renewable and non-renewable resources, including the role of stock constraints, regeneration functions, market power, property rights and the impact of technology. A fourth objective is to successfully use the well-known tool of cost-benefit analysis for environmental policy problems, which requires knowledge of the benefits of an improved natural environment. The last two objectives of the course are the acquisition of sufficient knowledge about the economics of sustainability and the application of environmental economic theory and policy at international level, e.g. to the problem of climate change. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | The course covers all the interactions between the economy and the natural environment. It introduces and explains basic welfare concepts and market failure; external effects, public goods, and environmental policy; the measurement of externalities and contingent valuation; the economics of non-renewable resources, renewable resources, cost-benefit-analysis, sustainability concepts; international aspects of resource and environmental problems; selected examples and case studies. After a general introduction to resource and environmental economics, highlighting its importace and the main issues, the course explains the normative basis, utilitarianism, and fairness according to different principles. Pollution externalities are a deep core topic of the lecture. We explain the governmental internalisation of externalities as well as the private internalisation of externalities (Coase theorem). Furthermore, the issues of free rider problems and public goods, efficient levels of pollution, tax vs. permits, and command and control instruments add to a thorough analysis of environmental policy. Turning to resource supply, the lecture first looks at empirical data on non-renewable natural resources and then develops the optimal price development (Hotelling-rule). It deals with the effects of explorations, new technologies, and market power. When treating the renewable resources, we look at biological growth functions, optimal harvesting of renewable resources, and the overuse of open-access resources. A next topic is cost-benefit analysis with the environment, requiring measuring environmental benefits and measuring costs. In the chapter on sustainability, the course covers concepts of sustainability, conflicts with optimality, and indicators of sustainability. In a final chapter, we consider international environmental problems and in particular climate change and climate policy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | Perman, R., Ma, Y., McGilvray, J, Common, M.: "Natural Resource & Environmental Economics", 4th edition, 2011, Harlow, UK: Pearson Education | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

363-0503-00L | Principles of MicroeconomicsGESS (Science in Perspective): This lecture is for MSc students only. BSc students register for 363-1109-00L Einführung in die Mikroökonomie. | W | 3 KP | 2G | M. Filippini | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | The course introduces basic principles, problems and approaches of microeconomics. This provides the students with reflective and contextual knowledge on how societies use scarce resources to produce goods and services and ensure a (fair) distribution. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | The learning objectives of the course are: (1) Students must be able to discuss basic principles, problems and approaches in microeconomics. (2) Students can analyse and explain simple economic principles in a market using supply and demand graphs. (3) Students can contrast different market structures and describe firm and consumer behaviour. (4) Students can identify market failures such as externalities related to market activities and illustrate how these affect the economy as a whole. (5) Students can also recognize behavioural failures within a market and discuss basic concepts related to behavioural economics. (6) Students can apply simple mathematical concepts on economic problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | The resources on our planet are finite. The discipline of microeconomics therefore deals with the question of how society can use scarce resources to produce goods and services and ensure a (fair) distribution. In particular, microeconomics deals with the behaviour of consumers and firms in different market forms. Economic considerations and discussions are not part of classical engineering and science study programme. Thus, the goal of the lecture "Principles of Microeconomics" is to teach students how economic thinking and argumentation works. The course should help the students to look at the contents of their own studies from a different perspective and to be able to critically reflect on economic problems discussed in the society. Topics covered by the course are: - Supply and demand - Consumer demand: neoclassical and behavioural perspective - Cost of production: neoclassical and behavioural perspective - Welfare economics, deadweight losses - Governmental policies - Market failures, common resources and public goods - Public sector, tax system - Market forms (competitive, monopolistic, monopolistic competitive, oligopolistic) - International trade | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | Lecture notes, exercises and reference material can be downloaded from Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | N. Gregory Mankiw and Mark P. Taylor (2020), "Economics", 5th edition, South-Western Cengage Learning. The book can also be used for the course 'Principles of Macroeconomics' (Sturm) For students taking only the course 'Principles of Microeconomics' there is a shorter version of the same book: N. Gregory Mankiw and Mark P. Taylor (2020), "Microeconomics", 5th edition, South-Western Cengage Learning. Complementary: R. Pindyck and D. Rubinfeld (2018), "Microeconomics", 9th edition, Pearson Education. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | GESS (Science in Perspective): This lecture is for MSc students only. BSc students register for 363-1109-00L Einführung in die Mikroökonomie. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kompetenzen |
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363-0565-00L | Principles of Macroeconomics | W | 3 KP | 2V | J.‑E. Sturm | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This course examines the behaviour of macroeconomic variables, such as gross domestic product, unemployment and inflation rates. It tries to answer questions like: How can we explain fluctuations of national economic activity? What can economic policy do against unemployment and inflation? | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | This lecture will introduce the fundamentals of macroeconomic theory and explain their relevance to every-day economic problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | This course helps you understand the world in which you live. There are many questions about the macroeconomy that might spark your curiosity. Why are living standards so meagre in many African countries? Why do some countries have high rates of inflation while others have stable prices? Why have some European countries adopted a common currency? These are just a few of the questions that this course will help you answer. Furthermore, this course will give you a better understanding of the potential and limits of economic policy. As a voter, you help choose the policies that guide the allocation of society's resources. When deciding which policies to support, you may find yourself asking various questions about economics. What are the burdens associated with alternative forms of taxation? What are the effects of free trade with other countries? How does the government budget deficit affect the economy? These and similar questions are always on the minds of policy makers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | The course webpage (to be found at Link) contains announcements, course information and lecture slides. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | The set-up of the course will closely follow the book of N. Gregory Mankiw and Mark P. Taylor (2020), Economics, Cengage Learning, Fifth Edition. This book can also be used for the course '363-0503-00L Principles of Microeconomics' (Filippini). Besides this textbook, the slides, lecture notes and problem sets will cover the content of the lecture and the exam questions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kompetenzen |
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363-1021-00L | Monetary Policy | W | 3 KP | 2V | J.‑E. Sturm, A. Rathke | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | The main aim of this course is to analyse the goals of monetary policy and to review the instruments available to central banks in order to pursue these goals. It will focus on the transmission mechanisms of monetary policy and the differences between monetary policy rules and discretionary policy. It will also make connections between theoretical economic concepts and current real world issues. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | This lecture will introduce the fundamentals of monetary economics and explain the working and impact of monetary policy. The main aim of this course is to describe and analyze the goals of monetary policy and to review the instruments available to central banks in order to pursue these goals. It will focus on the transmission mechanisms of monetary policy, the effectiveness of monetary policy actions, the differences between monetary policy rules and discretionary policy, as well as in institutional issues concerning central banks, transparency of monetary authorities and monetary policy in a monetary union framework. Moreover, we discuss the implementation of monetary policy in practice and the design of optimal policy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | For the functioning of today’s economy, central banks and their policies play an important role. Monetary policy is the policy adopted by the monetary authority of a country, the central bank. The central bank controls either the interest rate payable on very short-term borrowing or the money supply, often targeting inflation or the interest rate to ensure price stability and general trust in the currency. This monetary policy course looks into today’s major questions related to policies of central banks. It provides insights into the monetary policy process using core economic principles and real-world examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | The course webpage (to be found at Link) contains announcements, course information and lecture slides. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | The course will be based on chapters of: Mishkin, Frederic S. (2018), The Economics of Money, Banking and Financial Markets, 12th edition, Pearson. ISBN 9780134733821 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Basic knowledge in international economics and a good background in macroeconomics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kompetenzen |
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Finance | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

401-8905-00L | Financial Engineering (University of Zurich)Der Kurs muss direkt an der UZH als incoming student belegt werden. UZH Modulkürzel: MFOEC200 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 6 KP | 4G | Uni-Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This lecture is intended for students who would like to learn more on equity derivatives modelling and pricing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Quantitative models for European option pricing (including stochastic volatility and jump models), volatility and variance derivatives, American and exotic options. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | After introducing fundamental concepts of mathematical finance including no-arbitrage, portfolio replication and risk-neutral measure, we will present the main models that can be used for pricing and hedging European options e.g. Black- Scholes model, stochastic and jump-diffusion models, and highlight their assumptions and limitations. We will cover several types of derivatives such as European and American options, Barrier options and Variance- Swaps. Basic knowledge in probability theory and stochastic calculus is required. Besides attending class, we strongly encourage students to stay informed on financial matters, especially by reading daily financial newspapers such as the Financial Times or the Wall Street Journal. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | Script. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Basic knowledge of probability theory and stochastic calculus. Asset Pricing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

401-8913-00L | Advanced Corporate Finance I (University of Zurich)Der Kurs muss direkt an der UZH als incoming student belegt werden. UZH Modulkürzel: MOEC0455 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 6 KP | 4G | Uni-Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This course develops and refines tools for evaluating investments (capital budgeting), capital structure, and corporate securities. The course seeks to deepen students' understanding of the link between corporate finance theory and practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | This course develops and refines tools for evaluating investments (capital budgeting), capital structure, and corporate securities. With respect to capital structure, we start with the famous Miller and Modigliani irrelevance proposition and then move on to study the effects of taxes, bankruptcy costs, information asymmetries between firms and the capital markets, and agency costs. In this context, we will also study how leverage affects some central financial ratios that are often used in practice to assess firms and their stock. Other topics include corporate cash holdings, the use and pricing of convertible bonds, and risk management. The latter two topics involve option pricing. With respect to capital budgeting, the course pays special attention to tax effects in valuation, including in the estimation of the cost of capital. We will also study payout policy (dividends and share repurchases). The course seeks to deepen students' understanding of the link between corporate finance theory and practice. Various cases will be assigned to help reach this objective. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Topics covered 1. Capital structure: Perfect markets and irrelevance 2. Risk, leverage, taxes, and the cost of capital 3. Leverage and financial ratios 4. Payout policy: Dividends and share repurchases 5. Capital structure: Taxes and bankruptcy costs 6. Capital structure: Information asymmetries, agency costs, cash holdings 7. Valuation: DCF, adjusted present value and WACC 8. Valuation using options 9. The use and pricing of convertible bonds 10. Corporate risk management | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | This course replaces "Advanced Corporate Finance I" (MOEC0288), which will be discontinued from HS16. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Image Processing and Computer Vision | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

227-0447-00L | Image Analysis and Computer Vision | W | 6 KP | 3V + 1U | L. Van Gool, E. Konukoglu, F. Yu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | Course material Script, computer demonstrations, exercises and problem solutions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Information and Communication Technology | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

227-0105-00L | Introduction to Estimation and Machine Learning | W | 6 KP | 4G | H.‑A. Loeliger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Mathematical basics of estimation and machine learning, with a view towards applications in signal processing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | Students master the basic mathematical concepts and algorithms of estimation and machine learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Review of probability theory; basics of statistical estimation; least squares and linear learning; Hilbert spaces; Gaussian random variables; singular-value decomposition; kernel methods, neural networks, and more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | Lecture notes will be handed out as the course progresses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | solid basics in linear algebra and probability theory | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-0101-00L | Discrete-Time and Statistical Signal Processing | W | 6 KP | 4G | H.‑A. Loeliger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | 1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | Lecture Notes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-0417-00L | Information Theory I | W | 6 KP | 4G | A. Lapidoth | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equi-partition property, Huffman coding, channel capacity, the channel coding theorem, the source-channel separation theorem, and feedback capacity. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | The fundamentals of Information Theory including Shannon's source coding and channel coding theorems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | The entropy rate of a source, Typical sequences, the asymptotic equi-partition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the source-channel separation theorem, feedback capacity | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | T.M. Cover and J. Thomas, Elements of Information Theory (second edition) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Machine Learning Die Liste ist noch nicht vollständig. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

263-5210-00L | Probabilistic Artificial Intelligence | W | 8 KP | 3V + 2U + 2A | A. Krause | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | How can we build systems that perform well in uncertain environments? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as robotics. The course is designed for graduate students. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Topics covered: - Probability - Probabilistic inference (variational inference, MCMC) - Bayesian learning (Gaussian processes, Bayesian deep learning) - Probabilistic planning (MDPs, POMPDPs) - Multi-armed bandits and Bayesian optimization - Reinforcement learning | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Solid basic knowledge in statistics, algorithms and programming. The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

263-3210-00L | Deep Learning Number of participants limited to 320. | W | 8 KP | 3V + 2U + 2A | F. Perez Cruz, A. Lucchi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | In recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | This is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit. The participation in the course is subject to the following condition: - Students must have taken the exam in Advanced Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below: Advanced Machine Learning Link Computational Intelligence Lab Link Introduction to Machine Learning Link Statistical Learning Theory Link Computational Statistics Link Probabilistic Artificial Intelligence Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

252-3005-00L | Natural Language Processing Number of participants limited to 400. | W | 5 KP | 2V + 2U + 1A | R. Cotterell | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | This course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | The objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | Lectures will make use of textbooks such as the one by Jurafsky and Martin where appropriate, but will also make use of original research and survey papers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

263-5255-00L | Foundations of Reinforcement Learning Number of participants limited to 190. Last cancellation/deregistration date for this graded semester performance: Thursday, 28 October 2021! Please note that after that date no deregistration will be accepted and the course will be considered as "fail". | W | 5 KP | 2V + 2A | N. He | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Kurzbeschreibung | Reinforcement learning (RL) has been in the limelight of many recent breakthroughs in artificial intelligence. This course focuses on theoretical and algorithmic foundations of reinforcement learning, through the lens of optimization, modern approximation, and learning theory. The course targets M.S. students with strong research interests in reinforcement learning, optimization, and control. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lernziel | This course aims to provide students with an advanced introduction of RL theory and algorithms as well as bring them near the frontier of this active research field. By the end of the course, students will be able to - Identify the strengths and limitations of various reinforcement learning algorithms; - Formulate and solve sequential decision-making problems by applying relevant reinforcement learning tools; - Generalize or discover “new” applications, algorithms, or theories of reinforcement learning towards conducting independent research on the topic. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Inhalt | Basic topics include fundamentals of Markov decision processes, approximate dynamic programming, linear programming and primal-dual perspectives of RL, model-based and model-free RL, policy gradient and actor-critic algorithms, Markov games and multi-agent RL. If time allows, we will also discuss advanced topics such as batch RL, inverse RL, causal RL, etc. The course keeps strong emphasis on in-depth understanding of the mathematical modeling and theoretical properties of RL algorithms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Skript | Lecture notes will be posted on Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literatur | Dynamic Programming and Optimal Control, Vol I & II, Dimitris Bertsekas Reinforcement Learning: An Introduction, Second Edition, Richard Sutton and Andrew Barto. Algorithms for Reinforcement Learning, Csaba Czepesvári. Reinforcement Learning: Theory and Algorithms, Alekh Agarwal, Nan Jiang, Sham M. Kakade. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Voraussetzungen / Besonderes | Students are expected to have strong mathematical background in linear algebra, probability theory, optimization, and machine learning. |

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