Suchergebnis: Katalogdaten im Herbstsemester 2021
Data Science Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Interdisziplinäre Wahlfächer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-1033-00L | Neuromorphic Engineering I  Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module INI404 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | W | 6 KP | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Understanding of the characteristics of neuromorphic circuit elements. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0421-00L | Deep Learning in Artificial and Biological Neuronal Networks | W | 4 KP | 3G | B. Grewe | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Deep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers. After this course students will be able to: - read and understand the main ideas and methods that are presented in today’s neuroscience papers - explain the basic ideas and concepts of plasticity in the mammalian brain - implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks. - use a diverse set of ANN regularization methods to improve learning - simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Deep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | The lecture slides will be provided as a PDF after each lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used. The participation in the course is subject to the following conditions: 1) The number of participants is limited to 120 students (MSc and PhDs). 2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0945-00L | Cell and Molecular Biology for Engineers I This course is part I of a two-semester course. | W | 3 KP | 2G | C. Frei | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Lectures will include the following topics (part I and II): DNA, chromosomes, genome engineering, RNA, proteins, genetics, synthetic biology, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer and stem cells. In addition, 4 journal clubs will be held, where recent publications will be discussed (2 journal clubs in part I and 2 journal clubs in part II). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 40% for the final grade. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Scripts of all lectures will be available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 261-5100-00L | Computational Biomedicine Number of participants limited to 120. | W | 5 KP | 2V + 1U + 1A | V. Boeva, G. Rätsch | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Over the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine: 1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches. 2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes. 3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 261-5112-00L | Algorithms and Data Structures for Population Scale Genomics Findet dieses Semester nicht statt. Number of participants limited to 30. | W | 3 KP | 2G | Noch nicht bekannt | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Research in Biology and Medicine have been transformed into disciplines of applied data science over the past years. Not only size and inherentcomplexity of the data but also requirements on data privacy and complexity of search and access pose a wealth of new research questions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | This interactive course will explore the latest research on algorithms and data structures for population scale genomics applications and give insights into both the technical basis as well as the domain questions motivating it. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Over the duration of the semester, the course will cover three main topics. Each of the topics will consist of 70-80% lecture content and 20-30% seminar content. 1) Algorithms and data structures for text and graph compression. Motivated through applications in compressive genomics, the course will cover succinct indexing schemes for strings, trees and general graphs, compression schemes for binary matrices as well as the efficient representation of haplotypes and genomic variants. 2) Stochastic data structures and algorithms for approximate representation of strings and graphs as well as sets in general. This includes winnowing schemes and minimizers, sketching techniques, (minimal perfect) hashing and approximate membership query data structures. 3) Data structures supporting encryption and data privacy. As an extension to data structures discussed in the earlier topics, this will include secure indexing using homomorphic encryption as well as design for secure storage and distribution of data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 261-5111-00L | Asset Management: Advanced Investments (University of Zurich) Der Kurs muss direkt an der UZH als incoming student belegt werden. UZH Modulkürzel: MFOEC207 Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/deadlines.html | W | 3 KP | 2V | Uni-Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Comprehension and application of advanced portfolio theory | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Comprehension and application of advanced portfolio theory | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The theoretical part of the lecture consists of the topics listed below. - Standard Markowitz Model and Extensions MV Optimization, MV with Liabilities and CAPM. - The Crux with MV Resampling, regression, Black-Litterman, Bayesian, shrinkage, constrained and robust optimization. - Downside and Coherent Risk Measures Definition of risk measures, MV optimization under VaR and ES constraints. - Risk Budgeting Equal risk contribution, most diversified portfolio and other concentration indices - Regime Switching and Asset Allocation An introduction to regime switching models and its intuition. - Strategic Asset Allocation Introducing a continuous-time framework, solving the HJB equation and the classical Merton problem. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 http://www.cbb.ethz.ch/news-events.html 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 701-0023-00L | Atmosphäre | W | 3 KP | 2V | E. Fischer, T. Peter | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Grundlagen der Atmosphäre, physikalischer Aufbau und chemische Zusammensetzung, Spurengase, Kreisläufe in der Atmosphäre, Zirkulation, Stabilität, Strahlung, Kondensation, Wolken, Oxidationspotential und Ozonschicht. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Verständnis grundlegender physikalischer und chemischer Prozesse in der Atmosphäre. Kenntnis über die Mechanismen und Zusammenhänge von: Wetter - Klima, Atmosphäre - Ozeane - Kontinente, Troposphäre - Stratosphäre. Verständnis von umweltrelevanten Strukturen und Vorgängen in sehr unterschiedlichem Massstab. Grundlagen für eine modellmässige Darstellung komplexer Zusammenhänge in der Atmosphäre. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Grundlagen der Atmosphäre, physikalischer Aufbau und chemische Zusammensetzung, Spurengase, Kreisläufe in der Atmosphäre, Zirkulation, Stabilität, Strahlung, Kondensation, Wolken, Oxidationspotential und Ozonschicht. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Schriftliche Unterlagen werden abgegeben. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | - John H. Seinfeld and Spyros N. Pandis, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley, New York, 1998. - Gösta H. Liljequist, Allgemeine Meteorologie, Vieweg, Braunschweig, 1974. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 701-0473-00L | Wettersysteme | W | 3 KP | 2G | M. A. Sprenger, F. S. Scholder-Aemisegger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Satellitenbeobachtungen; Analyse vertikaler Sondierungen; Geostrophischer und thermischer Wind; Tiefdruckwirbel in den mittleren Breiten; globalen Zirkulation; Nordatlantische Oszillation; Atmosphärische Blockierungswetterlagen; Eulersche und Lagrange Perspektive der Dynamik; Potentielle Vortizität; Alpine Dynamik (Windstürme, Um- und Überströmung von Gebirgen); Planetare Grenzschicht | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Die Studierenden können: - die gängigen Mess- und Analysemethoden der Atmosphärendynamik erklären - mathematische Grundlagen der Atmosphärendynamik beispielhaft erklären - die Dynamik von globalen und synoptisch-skaligen Prozessen erklären - den Einfluss von Gebirgen auf die Atmosphärendynamik erklären - Grundverständnis der Rolle von feuchtdiabatischen Prozesse für Wettersysteme und warum stabile Wasserisotopen nützlich sind in diesem Zusammenhang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Satellitenbeobachtungen; Analyse vertikaler Sondierungen; Geostrophischer und thermischer Wind; Tiefdruckwirbel in den mittleren Breiten; Überblick und Energetik der globalen Zirkulation; Nordatlantische Oszillation; Atmosphärische Blockierungswetterlagen; Eulersche und Lagrange Perspektive der Dynamik; Potentielle Vortizität; Alpine Dynamik (Windstürme, Um- und Überströmung von Gebirgen); Planetare Grenzschicht | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Vorlesungsskript + Folien | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Atmospheric Science, An Introductory Survey John M. Wallace and Peter V. Hobbs, Academic Press | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 701-1251-00L | Land-Climate Dynamics Number of participants limited to 36. Priority is given to the target groups: - Master Environmental Science, - Master Atmospheric and Climate Science and - PhD D-USYS until September 20th,2021. Waiting list will be deleted September 27th, 2021. | W | 3 KP | 2G | S. I. Seneviratne, R. Padrón Flasher | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The purpose of this course is to provide fundamental background on the role of land surface processes (vegetation, soil moisture dynamics, land energy and water balances) in the climate system. The course consists of 2 contact hours per week, including lectures, group projects and computer exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students can understand the role of land processes and associated feedbacks in the climate system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Powerpoint slides will be made available | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: Introductory lectures in atmospheric and climate science Atmospheric physics -> Link and/or Climate systems -> Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0417-00L | Transport Planning Methods | W | 6 KP | 4G | K. W. Axhausen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course provides the necessary knowledge to develop models supporting and also evaluating the solution of given planning problems. The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | - Knowledge and understanding of statistical methods and algorithms commonly used in transport planning - Comprehend the reasoning and capabilities of transport models - Ability to independently develop a transport model able to solve / answer planning problem - Getting familiar with cost-benefit analysis as a decision-making supporting tool | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course provides the necessary knowledge to develop models supporting the solution of given planning problems and also introduces cost-benefit analysis as a decision-making tool. Examples of such planning problems are the estimation of traffic volumes, prediction of estimated utilization of new public transport lines, and evaluation of effects (e.g. change in emissions of a city) triggered by building new infrastructure and changes to operational regulations. To cope with that, the problem is divided into sub-problems, which are solved using various statistical models (e.g. regression, discrete choice analysis) and algorithms (e.g. iterative proportional fitting, shortest path algorithms, method of successive averages). The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis. Interim lab session take place regularly to guide and support students with the applied part of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Moodle platform (enrollment needed) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Willumsen, P. and J. de D. Ortuzar (2003) Modelling Transport, Wiley, Chichester. Cascetta, E. (2001) Transportation Systems Engineering: Theory and Methods, Kluwer Academic Publishers, Dordrecht. Sheffi, Y. (1985) Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods, Prentice Hall, Englewood Cliffs. Schnabel, W. and D. Lohse (1997) Verkehrsplanung, 2. edn., vol. 2 of Grundlagen der Strassenverkehrstechnik und der Verkehrsplanung, Verlag für Bauwesen, Berlin. McCarthy, P.S. (2001) Transportation Economics: A case study approach, Blackwell, Oxford. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0491-00L | Agent Based Modeling in Transportation | W | 6 KP | 4G | M. Balac | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides an introduction to agent-based modeling in transportation. The lectures and exercises offer an opportunity to learn about agent-based models' current methodology, focusing on MATSim, how agent-based models are set up, and perform a practical case study by working in teams. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | At the end of the course, the students should: - have an understanding of agent-based modeling - have an understanding of MATSim - have an understanding of the process needed to set up an agent-based study - have practical experience of using MATSim to perform practical transportation studies | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course provides an introduction to agent-based models for transportation policy analysis. Four essential topics are covered: 1) Introduction of agent-based modeling and its comparison to the traditional state of practice modeling 2) Introduction of MATSim, an open-source agent-based model, developed at ETH Zurich and TU Berlin, and its various parts 3) Setting up an agent-based model simulation, where different statistical methods used in the process will be introduced and explained. Here the open-source eqasim framework used at ETH Zurich to set up agent-based models will be introduced 4) Conducting a transport policy study. The case study will be performed in groups and will include a paper-like report. During the course, outside lecturers will give several lectures on using MATSim in practice (i.e., SBB). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Agent-based modeling in general Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences, 99(suppl 3), 7280-7287. Helbing, D (2012) Social Self-Organization, Understanding Complex Systems, Springer, Berlin. Heppenstall, A., A. T. Crooks, L. M. See and M. Batty (2012) Agent-Based Models of Geographical Systems, Springer, Dordrecht. MATSim Horni, A., K. Nagel and K.W. Axhausen (eds.) (2016) The Multi-Agent Transport Simulation MATSim, Ubiquity, London (http://www.matsim.org/the-book) Additional relevant readings, primarily scientific articles, will be recommended throughout the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | There are no strict preconditions in terms of which lectures the students should have previously attended. However, knowledge of basic statistical theory is expected, and experience with at least one high-level programming language (Java, R, Python, or other) is recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0227-00L | Cartography III | W | 5 KP | 4G | L. Hurni | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This follow-up course proceeds to a complete Web map project and introduces in 3D and animated cartography. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | This course enables students to plan, design and realize interactive Web map projects. The introduction to 3D and animated cartography also provides a general knowledge about animated 3D graphics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | - Web mapping. - Data processing. - Interaction design. - Graphical user interface. - 3D cartography. - Animated cartography. - Video production. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handouts of the lectures and exercise documents are available on Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Further information at http://www.karto.ethz.ch/studium/lehrangebot.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0237-00L | GIS III | W | 5 KP | 3G | W. Kuhn | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course deals with advanced topics in GIS, such as Business aspects and Legal issues; Geostatistics; Human-Computer Interaction; Cognitive Issues in GIS; Geosensors; Spatial Data Mining and Machine Learning for GIS. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students will get a detailed overview of advanced GIS topics. They will work on a small project with geosensors in the lab and perform practical tasks relating to Geostatistics and Machine Learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture slides will be made available in digital form. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0717-00L | Geoinformationstechnologien und -analysen | W | 6 KP | 5G | W. Kuhn | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Geoinformationstechnologien und -analysen für Fortgeschrittene: Mobile GIS; Web-GIS & Geo-Web-Services; Spatial Big Data; Zeitliche Aspekte in GIS; Analyse von Bewegungsdaten; Benutzerschnittstellen Übungen: Web-GIS-Semesterprojekt in Gruppenarbeit | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Fortgeschrittene Geoinformationstechnologien (Mobile GIS und Web-GIS) und raum-zeitliche Analysemethoden kennen, um Projekte im Zusammenhang mit Realisierung, Nutzung und Betrieb von Web-GIS ingenieurmässig planen und implementieren zu können. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | - Mobile GIS - Web-GIS & Geo-Web-Services - Spatial Big Data - Zeitliche Aspekte in GIS - Analyse von Bewegungsdaten - Benutzerschnittstellen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Vorlesungspräsentationen werden digital zur Verfügung gestellt. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Bill, R. (2016). Grundlagen der Geo-Informationssysteme (6. Auflage): Wichmann. Bartelme, N. (2005). Geoinformatik - Modelle, Strukturen, Funktionen (4. Auflage). Berlin: Springer. O'Sullivan, D., & Unwin, D. (2010). Geographic Information Analysis (2nd Edition). Wiley. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | GIS GZ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0778-00L | GIS and Geoinformatics Lab | W | 4 KP | 3P | P. Kiefer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Independent study project with novel geoinformation technologies. Information on past projects: http://gis-lab.ethz.ch/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | This lab focuses on presenting spatial, temporal, and open data in tangible ways. Students will learn how to work with novel geoinformation technologies such as virtual/mixed reality or mobile applications. They will engage in teamwork, application design, programming and presenting their results. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0575-00L | Advanced Topics in Communication Networks | W | 6 KP | 2V + 2U | L. Vanbever | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course covers advanced topics and technologies in computer networks, both theoretically and practically. It is offered each Fall semester, with rotating topics. Repetition for credit is possible with consent of the instructor. In the Fall 2021, the course will cover advanced topics in Internet routing and forwarding. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The goals of this course is to provide students with a deeper understanding of the existing and upcoming Internet routing and forwarding technologies used in large-scale computer networks such as Internet Service Providers (e.g., Swisscom or Deutsche Telekom), Content Delivery Networks (e.g., Netflix) and Data Centers (e.g., Google). Besides covering the fundamentals, the course will be “hands-on” and will enable students to play with the technologies in realistic network environments, and even implement some of them on their own during labs and a final group project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course will cover advanced topics in Internet routing and forwarding such as: - Tunneling - Hierarchical routing - Traffic Engineering and Load Balancing - Virtual Private Networks - Quality of Service/Queuing/Scheduling - Fast Convergence - Network virtualization - Network programmability (OpenFlow, P4) - Network measurements The course will be divided in two main blocks. The first block (~8 weeks) will interleave classical lectures with practical exercises and labs. The second block (~6 weeks) will consist of a practical project which will be performed in small groups (~3 students). During the second block, lecture slots will be replaced by feedback sessions where students will be able to ask questions and get feedback about their project. The last week of the semester will be dedicated to student presentations and demonstrations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes and material will be made available before each course on the course website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Relevant references will be made available through the course website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: Communication Networks (227-0120-00L) or equivalents / good programming skills (in any language) are expected as both the exercices and the final project will involve coding. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 401-3922-00L | Life Insurance Mathematics | W | 4 KP | 2V | M. Koller | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The classical life insurance model is presented together with the important insurance types (insurance on one and two lives, term and endowment insurance and disability). Besides that the most important terms such as mathematical reserves are introduced and calculated. The profit and loss account and the balance sheet of a life insurance company is explained and illustrated. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 401-3925-00L | Non-Life Insurance: Mathematics and Statistics | W | 8 KP | 4V + 1U | M. V. Wüthrich | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The lecture aims at providing a basis in non-life insurance mathematics which forms a core subject of actuarial science. It discusses collective risk modeling, individual claim size modeling, approximations for compound distributions, ruin theory, premium calculation principles, tariffication with generalized linear models and neural networks, credibility theory, claims reserving and solvency. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The student is familiar with the basics in non-life insurance mathematics and statistics. This includes the basic mathematical models for insurance liability modeling, pricing concepts, stochastic claims reserving models and ruin and solvency considerations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The following topics are treated: Collective Risk Modeling Individual Claim Size Modeling Approximations for Compound Distributions Ruin Theory in Discrete Time Premium Calculation Principles Tariffication Generalized Linear Models and Neural Networks Bayesian Models and Credibility Theory Claims Reserving Solvency Considerations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | M.V. Wüthrich, Non-Life Insurance: Mathematics & Statistics http://ssrn.com/abstract=2319328 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | M.V. Wüthrich, M. Merz. Statistical Foundations of Actuarial Learning and its Applications http://ssrn.com/abstract=3822407 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The exams ONLY take place during the official ETH examination period. This course will be held in English and counts towards the diploma of "Aktuar SAV". For the latter, see details under www.actuaries.ch. Prerequisites: knowledge of probability theory, statistics and applied stochastic processes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 401-3928-00L | Reinsurance Analytics | W | 4 KP | 2V | P. Antal, P. Arbenz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides an introduction to reinsurance from an actuarial perspective. The objective is to understand the fundamentals of risk transfer through reinsurance and models for extreme events such as natural or man-made catastrophes. The lecture covers reinsurance contracts, Experience and Exposure pricing, natural catastrophe modelling, solvency regulation, and insurance linked securities | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | This course provides an introduction to reinsurance from an actuarial perspective. The objective is to understand the fundamentals of risk transfer through reinsurance and the mathematical approaches associated with low frequency high severity events such as natural or man-made catastrophes. Topics covered include: - Reinsurance Contracts and Markets: Different forms of reinsurance, their mathematical representation, history of reinsurance, and lines of business. - Experience Pricing: Modelling of low frequency high severity losses based on historical data, and analytical tools to describe and understand these models - Exposure Pricing: Loss modelling based on exposure or risk profile information, for both property and casualty risks - Natural Catastrophe Modelling: History, relevance, structure, and analytical tools used to model natural catastrophes in an insurance context - Solvency Regulation: Regulatory capital requirements in relation to risks, effects of reinsurance thereon, and differences between the Swiss Solvency Test and Solvency 2 - Insurance linked securities: Alternative risk transfer techniques such as catastrophe bonds | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course provides an introduction to reinsurance from an actuarial perspective. The objective is to understand the fundamentals of risk transfer through reinsurance and the mathematical approaches associated with low frequency high severity events such as natural or man-made catastrophes. Topics covered include: - Reinsurance Contracts and Markets: Different forms of reinsurance, their mathematical representation, history of reinsurance, and lines of business. - Experience Pricing: Modelling of low frequency high severity losses based on historical data, and analytical tools to describe and understand these models - Exposure Pricing: Loss modelling based on exposure or risk profile information, for both property and casualty risks - Natural Catastrophe Modelling: History, relevance, structure, and analytical tools used to model natural catastrophes in an insurance context - Solvency Regulation: Regulatory capital requirements in relation to risks, effects of reinsurance thereon, and differences between the Swiss Solvency Test and Solvency 2 - Insurance linked securities: Alternative risk transfer techniques such as catastrophe bonds | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Slides and lecture notes will be made available. An excerpt of last year's lecture notes is available here: https://sites.google.com/site/philipparbenz/reinsuranceanalytics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Basic knowledge in statistics, probability theory, and actuarial techniques | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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