Suchergebnis: Katalogdaten im Frühjahrssemester 2023
Computational Biology and Bioinformatics Master  More informations at: https://www.cbb.ethz.ch | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 KernfächerPlease note that the list of core courses is a closed list. Other courses cannot be added to the core course category in the study plan. Also the assignments of courses to core subcategories cannot be changed. Students need to pass at least one course in each core subcategory. A total of 40 ECTS needs to be acquired in the core course category. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Bioinformatics Please note that all Bioinformatics core courses are offered in the autumn semester | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Biophysics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 262-5100-00L | Protein Biophysics (University of Zurich) Der Kurs muss direkt an der UZH als incoming student belegt werden. UZH Modulkürzel: BCH304 Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/deadlines.html | W | 6 KP | 3V + 1U | Uni-Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course includes a general introduction into protein structure and biophysics as well as into the usage of molecular dynamics simulations and other computational methods, protein structure and X-ray techniques, protein NMR for determining protein structure and dynamics as well as for folding studies and protein thermodynamics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | A 4 hour/week course on all aspects of protein biophysics. The course includes a general introduction into protein structure and biophysics as well as into the usage of molecular dynamics simulations and other computational methods, protein structure and X-ray techniques, protein NMR for determining protein structure and dynamics as well as for folding studies and protein thermodynamics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The lecture course consists of four parts: 1) non-covalent interactions, properties of water and hydrophobic effect, protein folding and misfolding, molecular dynamics simulations; 2) atomistic simulations of proteins 3) thermodynamics and kinetics of protein folding; 4) single molecule biophysics: single molecule fluorescence spectroscopy, fluorescence correlation spectroscopy, and applications to stochastic processes in biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0980-00L | Biofluiddynamics | W | 4 KP | 2V + 1U | D. Obrist, P. Jenny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction to the fluid dynamics of the human body and the modeling of physiological flow processes (biomedical fluid dynamics). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | A basic understanding of fluid dynamical processes in the human body. Knowledge of the basic concepts of fluid dynamics and the ability to apply these concepts appropriately. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This lecture is an introduction to the fluid dynamics of the human body (biomedical fluid dynamics). For selected topics of human physiology, we introduce fundamental concepts of fluid dynamics (e.g., creeping flow, incompressible flow, flow in porous media, flow with particles, fluid-structure interaction) and use them to model physiological flow processes. The list of studied topics includes the cardiovascular system and related diseases, blood rheology, microcirculation, respiratory fluid dynamics and fluid dynamics of the inner ear. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes are provided electronically. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | A list of books on selected topics of biofluiddynamics can be found on the course web page. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Biosystems | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-0016-00L | Computational Systems Biology: Stochastic Approaches | W | 4 KP | 3G | M. H. Khammash, A. Gupta | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course is concerned with the development of computational methods for modeling, simulation, and analysis of stochasticity in living cells. Using these tools, the course explores the richness of stochastic phenomena, how it arises from the interactions of dynamics and noise, and its biological implications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | To understand the origins and implications of stochastic noise in living cells, and to learn the computational tools for the modeling, simulation, analysis, and identification of stochastic biochemical reaction networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The cellular environment is abuzz with noise. A key source of this noise is the randomness that characterizes the motion of cellular constituents at the molecular level. Cellular noise not only results in random fluctuations (over time) within individual cells, but it is also a main source of phenotypic variability among clonal cell populations. Review of basic probability and stochastic processes; Introduction to stochastic gene expression; deterministic vs. stochastic models; the stochastic chemical kinetics framework; a rigorous derivation of the chemical master equation; moment computations; linear vs. nonlinear propensities; linear noise approximations; Monte Carlo simulations; Gillespie's Stochastic Simulation Algorithm (SSA) and variants; direct methods for the solution of the Chemical Master Equation; moment closure methods; intrinsic and extrinsic noise in gene expression; parameter identification from noise; propagation of noise in cell networks; noise suppression in cells; the role of feedback; exploiting noise; bimodality and stochastic switches. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Literature will be distributed during the course as needed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Students are expected to have completed the course `Mathematical modeling for systems biology (BSc Biotechnology) or `Computational systems biology (MSc Computational biology and bioinformatics). Concurrent enrollment in `Computational Systems Biology: Deterministic Approaches is recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-0111-00L | Synthetic Biology I | W | 4 KP | 3G | S. Panke, J. Stelling | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Theoretical & practical introduction into the design of dynamic biological systems at different levels of abstraction, ranging from biological fundamentals of systems design (introduction to bacterial gene regulation, elements of transcriptional & translational control, advanced genetic engineering) to engineering design principles (standards, abstractions) mathematical modelling & systems desig | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After the course, students will be able to theoretically master the biological and engineering fundamentals required for biological design to be able to participate in the international iGEM competition. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The overall goal of the course is to familiarize the students with the potential, the requirements and the problems of designing dynamic biological elements that are of central importance for manipulating biological systems, primarily (but not exclusively) prokaryotic systems. Next, the students will be taken through a number of successful examples of biological design, such as toggle switches, pulse generators, and oscillating systems, and apply the biological and engineering fundamentals to these examples, so that they get hands-on experience on how to integrate the various disciplines on their way to designing biological systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handouts during classes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Mark Ptashne, A Genetic Switch (3rd ed), Cold Spring Haror Laboratory Press Uri Alon, An Introduction to Systems Biology, Chapman & Hall | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | 1) Though we do not place a formal requirement for previous participation in particular courses, we expect all participants to be familiar with a certain level of biology and of mathematics. Specifically, there will be material for self study available on https://bsse.ethz.ch/bpl/education/lectures/synthetic-biology-i/download.html as of mid January, and everybody is expected to be fully familiar with this material BEFORE THE CLASS BEGINS to be able to follow the different lectures. Please contact sven.panke@bsse.ethz.ch for access to material 2) The course is also thought as a preparation for the participation in the international iGEM synthetic biology summer competition (www.syntheticbiology.ethz.ch, http://www.igem.org). This competition is also the contents of the course Synthetic Biology II. https://bsse.ethz.ch/bpl/education/lectures/synthetic-biology-i/download.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| Data Science | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 551-0364-00L | Functional Genomics Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module BIO 254 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | W | 3 KP | 2V | C. von Mering, B. Bodenmiller, M. Gstaiger, H. Rehrauer, R. Schlapbach, K. Shimizu, N. Zamboni, weitere Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Functional genomics is key to understanding the dynamic aspects of genome function and regulation. Functional genomics approaches use the wealth of data produced by large-scale DNA sequencing, gene expression profiling, proteomics and metabolomics. Today functional genomics is becoming increasingly important for the generation and interpretation of quantitative biological data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Functional genomics is key to understanding the dynamic aspects of genome function and regulation. Functional genomics approaches use the wealth of data produced by large-scale DNA sequencing, gene expression profiling, proteomics and metabolomics. Today functional genomics is becoming increasingly important for the generation and interpretation of quantitative biological data. Such data provide the basis for systems biology efforts to elucidate the structure, dynamics and regulation of cellular networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The curriculum of the Functional Genomics course emphasizes an in depth understanding of new technology platforms for modern genomics and advanced genetics, including the application of functional genomics approaches such as advanced sequencing, proteomics, metabolomics, clustering and classification. Students will learn quality controls and standards (benchmarking) that apply to the generation of quantitative data and will be able to analyze and interpret these data. The training obtained in the Functional Genomics course will be immediately applicable to experimental research and design of systems biology projects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The Functional Genomics course will be taught in English. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-0702-00L | Statistical Models in Computational Biology | W | 6 KP | 2V + 1U + 2A | J. Kuipers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Graphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | - Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252 - Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007. - Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-6190-00L | Machine Learning Der Kurs muss direkt an der Uni Basel belegt werden. UBas Kursnummer 17165 Beachten Sie die Einschreibungstermine und -Modalitäten an der Uni Basel: Link | W | 8 KP | 4G | externe Veranstalter | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0220-00L | Introduction to Machine Learning  Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch | W | 8 KP | 4V + 2U + 1A | A. Krause, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course introduces the foundations of learning and making predictions based on data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | - Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| Seminar Compulsory seminar. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-0704-00L | Computational Biology and Bioinformatics Seminar The seminar is addressed primarily at students enrolled in the MSc CBB programme. Students of other ETH study programmes interested in this course need to ask the lecturer for permission to enrol in the course. The Seminar will be offered in autumn semester in Basel (involving professors and lecturers from the University of Basel) and in spring semester in Zurich (involving professors and lecturers from the University of Zurich). Professors and lecturers from ETH Zurich are involved in both semesters. | O | 2 KP | 2S | J. Stelling, D. Iber, M. H. Khammash, J. Payne | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Computational Biology und Bioinformatik analysieren lebende Systeme mit Methoden der Informatik. Das Seminar kombiniert Präsentationen von Studierenden und Forschenden, um das sich schnell entwickelnde Gebiet aus der Informatikperspektive zu skizzieren. Themenbereiche sind Sequenzanalyse, Proteomics, Optimierung und Bio-inspired computing, Systemmodellierung, -simulation und -analyse. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Studying and presenting fundamental papers of Computational Biology and Bioinformatics. Learning how to make a scientific presentation and how classical methods are used or further developed in current research. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Computational biology and bioinformatics aim at advancing the understanding of living systems through computation. The complexity of these systems, however, provides challenges for software and algorithms, and often requires entirely novel approaches in computer science. The aim of the seminar is to give an overview of this rapidly developing field from a computer science perspective. In particular, it will focus on the areas of (i) DNA sequence analysis, sequence comparison and reconstruction of phylogenetic trees, (ii) protein identification from experimental data, (iii) optimization and bio-inspired computing, and (iv) systems analysis of complex biological networks. The seminar combines the discussion of selected research papers with a major impact in their domain by the students with the presentation of current active research projects / open challenges in computational biology and bioinformatics by the lecturers. Each week, the seminar will focus on a different topic related to ongoing research projects at ETHZ, thus giving the students the opportunity of obtaining knowledge about the basic research approaches and problems as well as of gaining insight into (and getting excited about) the latest developments in the field. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Original papers to be presented by the students will be provided in the first week of the seminar. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Vertiefungsfächer A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof at least 16 ECTS in the Theory and 10 ECTS in the Biology category. Note that some of the lectures are being recorded: https://video.ethz.ch/lectures.html | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Theorie At least 16 ECTS need to be acquired in this category. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0063-00L | Data Modelling and Databases | W | 7 KP | 4V + 2U | G. Alonso, C. Zhang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Data modelling (Entity Relationship), relational data model, relational design theory (normal forms), SQL, database integrity, transactions and advanced database engines | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Introduction to relational databases and data management. Basics of SQL programming and transaction management. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course covers the basic aspects of the design and implementation of databases and information systems. The courses focuses on relational databases as a starting point but will also cover data management issues beyond databases such as: transactional consistency, replication, data warehousing, other data models, as well as SQL. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Kemper, Eickler: Datenbanksysteme: Eine Einführung. Oldenbourg Verlag, 7. Auflage, 2009. Garcia-Molina, Ullman, Widom: Database Systems: The Complete Book. Pearson, 2. Auflage, 2008. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 401-0674-00L | Numerical Methods for Partial Differential Equations Nicht für Studierende BSc/MSc Mathematik | W | 10 KP | 2G + 2U + 2P + 4A | R. Hiptmair | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Derivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations: convection-diffusion, heat equation, wave equation, conservation laws. Implementation in C++ based on a finite element library. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Main skills to be acquired in this course: * Ability to implement fundamental numerical methods for the solution of partial differential equations efficiently. * Ability to modify and adapt numerical algorithms guided by awareness of their mathematical foundations. * Ability to select and assess numerical methods in light of the predictions of theory * Ability to identify features of a PDE (= partial differential equation) based model that are relevant for the selection and performance of a numerical algorithm. * Ability to understand research publications on theoretical and practical aspects of numerical methods for partial differential equations. * Skills in the efficient implementation of finite element methods on unstructured meshes. This course is neither a course on the mathematical foundations and numerical analysis of methods nor an course that merely teaches recipes and how to apply software packages. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Second-order scalar elliptic boundary value problems Finite-element methods (FEM) FEM: Convergence and Accuracy Non-linear elliptic boundary value problems Second-order linear evolution problems Convection-diffusion problems Numerical methods for conservation laws | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | The lecture will be taught in flipped classroom format: - Video tutorials for all thematic units will be published online. - Tablet notes accompanying the videos will be made available to the audience as PDF. - A comprehensive lecture document will cover all aspects of the course, see https://www.sam.math.ethz.ch/~grsam/NUMPDEFL/NUMPDE.pdf | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Chapters of the following books provide supplementary reading (detailed references in course material): * D. Braess: Finite Elemente, Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Springer 2007 (available online). * S. Brenner and R. Scott. Mathematical theory of finite element methods, Springer 2008 (available online). * A. Ern and J.-L. Guermond. Theory and Practice of Finite Elements, volume 159 of Applied Mathematical Sciences. Springer, New York, 2004. * Ch. Großmann and H.-G. Roos: Numerical Treatment of Partial Differential Equations, Springer 2007. * W. Hackbusch. Elliptic Differential Equations. Theory and Numerical Treatment, volume 18 of Springer Series in Computational Mathematics. Springer, Berlin, 1992. * P. Knabner and L. Angermann. Numerical Methods for Elliptic and Parabolic Partial Differential Equations, volume 44 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * S. Larsson and V. Thomée. Partial Differential Equations with Numerical Methods, volume 45 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * R. LeVeque. Finite Volume Methods for Hyperbolic Problems. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge, UK, 2002. However, study of supplementary literature is not important for for following the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Mastery of basic calculus and linear algebra is taken for granted. Familiarity with fundamental numerical methods (solution methods for linear systems of equations, interpolation, approximation, numerical quadrature, numerical integration of ODEs) is essential. Important: Coding skills and experience in C++ are essential. Homework assignments involve substantial coding, partly based on a C++ finite element library. The written examination will be computer based and will comprise coding tasks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 401-3052-05L | Introduction to Graph Theory This is the first half of the course unit 401-3052-10L Graph Theory. Notice that at most one of the two course units 401-3052-05L Introduction to Graph Theory and 401-3052-10L Graph Theory can be recognised for credits. | W | 5 KP | 2V + 1U | B. Sudakov | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Basic notions, trees, spanning trees, Caley's formula, vertex and edge connectivity, 2-connectivity, Mader's theorem, Menger's theorem, Eulerian graphs, Hamilton cycles, Dirac's theorem, matchings, theorems of Hall, König and Tutte, planar graphs, Euler's formula, basic non-planar graphs, graph colorings, greedy colorings, Brooks' theorem, 5-colorings of planar graphs | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students will get an overview over the most fundamental questions concerning graph theory. We expect them to understand the proof techniques and to use them autonomously on related problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture will be only at the blackboard. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | West, D.: "Introduction to Graph Theory" Diestel, R.: "Graph Theory" Further literature links will be provided in the lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Students are expected to have a mathematical background and should be able to write rigorous proofs. NOTICE: This course unit was previously offered as 252-1408-00L Graphs and Algorithms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0558-00L | Principles of Distributed Computing | W | 7 KP | 2V + 2U + 2A | R. Wattenhofer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Distributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Distributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems. Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Lecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world. Mastering Distributed Algorithms Roger Wattenhofer Inverted Forest Publishing, 2020. ISBN 979-8628688267 Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Course pre-requisites: Interest in algorithmic problems. (No particular course needed.) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 401-3632-00L | Computational Statistics | W | 8 KP | 3V + 1U | M. Mächler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | See the class website | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | At least one semester of (basic) probability and statistics. Programming experience is helpful but not required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 101-0178-01L | Uncertainty Quantification in Engineering | W | 3 KP | 2G | N. Lüthen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Uncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation techniques (Monte Carlo simulation, polynomial chaos expansions), and sensitivity analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After this course students will be able to properly pose an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. The course is suitable for any master/Ph.D. student in engineering or natural sciences, physics, mathematics, computer science with a basic knowledge of probability theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the in-house software UQLab (www.uqlab.com). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Detailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | A basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course. A good knowledge of Matlab is required to participate in the tutorials and for the mini-project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 252-0526-00L | Statistical Learning Theory | W | 8 KP | 3V + 2U + 2A | J. M. Buhmann | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course covers advanced methods of statistical learning: - Variational methods and optimization. - Deterministic annealing. - Clustering for diverse types of data. - Model validation by information theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course surveys recent methods of statistical learning. The fundamentals of machine learning, as presented in the courses "Introduction to Machine Learning" and "Advanced Machine Learning", are expanded from the perspective of statistical learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | - Variational methods and optimization. We consider optimization approaches for problems where the optimizer is a probability distribution. We will discuss concepts like maximum entropy, information bottleneck, and deterministic annealing. - Clustering. This is the problem of sorting data into groups without using training samples. We discuss alternative notions of "similarity" between data points and adequate optimization procedures. - Model selection and validation. This refers to the question of how complex the chosen model should be. In particular, we present an information theoretic approach for model validation. - Statistical physics models. We discuss approaches for approximately optimizing large systems, which originate in statistical physics (free energy minimization applied to spin glasses and other models). We also study sampling methods based on these models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | A draft of a script will be provided. Lecture slides will be made available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001. L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Knowledge of machine learning (introduction to machine learning and/or advanced machine learning) Basic knowledge of statistics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0216-00L | Computational Control Previously (up until FS22) named "Control Systems II" | W | 6 KP | 2V + 2U | S. Bolognani | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The focus of the course is on the design of advanced controllers for cyber-physical systems, that is, systems in which the controller is an embedded computer that can sense and actuate a physical plant. Advanced computational control strategies like Model Predictive Control, Reinforcement Learning, and Data-Driven control will be covered. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The objective of the course is to prepare students to the design of advanced digital control systems: this includes comparing alternative control strategies, deciding what class of controllers to employ for a specific problem, tune the controller in order to meet the desired specifications, and produce a conceptual design of how the controller can be implemented and deployed. Simplifying assumptions on the underlying plant that were made in the course Control Systems are relaxed, and advanced computational control concepts and techniques are presented. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course will cover both the challenges of a digital control system and the many possibilities offered by powerful computation in control. Different aspects and challenges of embedded control of cyber-physical systems will be discussed. We will then review the limitations of classical control strategies like PID control and LQR control, and motivate the need for controllers that employ significant real-time computation. In particular, we will look into Model Predictive Control, Reinforcement Learning, Data-Driven control, and possibly other advanced computational control techniques. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes will be available on the Moodle page of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | References to the literature will be provided during the course. No textbook is necessary, but students are encouraged to read the suggested readings. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: Control Systems or equivalent. A background in optimization is very helpful. Students that don’t have it will be provided with some additional reading material. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 151-0566-00L | Recursive Estimation | W | 4 KP | 2V + 1U | R. D'Andrea | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Learn the basic recursive estimation methods and their underlying principles. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Requirements: Introductory probability theory and matrix-vector algebra. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 401-3642-00L | Brownian Motion and Stochastic Calculus | W | 10 KP | 4V + 1U | D. Possamaï | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course gives an introduction to Brownian motion and stochastic calculus. It includes the construction and properties of Brownian motion, basics of Markov processes in continuous time and of Levy processes, and stochastic calculus for continuous semimartingales. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | This course gives an introduction to Brownian motion and stochastic calculus. The following topics are planned: - Definition and construction of Brownian motion - Some important properties of Brownian motion - Basics of Markov processes in continuous time - Stochastic calculus, including stochastic integration for continuous semimartingales, Ito's formula, Girsanov's theorem, stochastic differential equations and connections with partial differential equations - Basics of Levy processes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes will be made available in class. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | - R.F. Bass, Stochastic Processes, Cambidge University Press (2001). - I. Karatzas, S. Shreve, Brownian Motion and Stochastic Calculus, Springer (1991). - J.-F. Le Gall, Brownian Motion, Martingales, and Stochastic Calculus, Springer (2016). - D. Revuz, M. Yor, Continuous Martingales and Brownian Motion, Springer (2005). - L.C.G. Rogers, D. Williams, Diffusions, Markov Processes and Martingales, vol. 1 and 2, Cambridge University Press (2000). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Familiarity with measure-theoretic probability as in the standard D-MATH course "Probability Theory" will be assumed. Textbook accounts can be found for example in - J. Jacod, P. Protter, Probability Essentials, Springer (2004). - R. Durrett, Probability: Theory and Examples, Cambridge University Press (2010). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 401-3602-00L | Applied Stochastic Processes | W | 8 KP | 3V + 1U | V. Tassion | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Poisson-Prozesse; Erneuerungsprozesse; Markovketten in diskreter und in stetiger Zeit; einige Beispiele und Anwendungen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Stochastische Prozesse dienen zur Beschreibung der Entwicklung von Systemen, die sich in einer zufälligen Weise entwickeln. In dieser Vorlesung bezieht sich die Entwicklung auf einen skalaren Parameter, der als Zeit interpretiert wird, so dass wir die zeitliche Entwicklung des Systems studieren. Die Vorlesung präsentiert mehrere Klassen von stochastischen Prozessen, untersucht ihre Eigenschaften und ihr Verhalten und zeigt anhand von einigen Beispielen, wie diese Prozesse eingesetzt werden können. Die Hauptbetonung liegt auf der Theorie; "applied" ist also im Sinne von "applicable" zu verstehen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | R. N. Bhattacharya and E. C. Waymire, "Stochastic Processes with Applications", SIAM (2009), available online: http://epubs.siam.org/doi/book/10.1137/1.9780898718997 R. Durrett, "Essentials of Stochastic Processes", Springer (2012), available online: http://link.springer.com/book/10.1007/978-1-4614-3615-7/page/1 M. Lefebvre, "Applied Stochastic Processes", Springer (2007), available online: http://link.springer.com/book/10.1007/978-0-387-48976-6/page/1 S. I. Resnick, "Adventures in Stochastic Processes", Birkhäuser (2005) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites are familiarity with (measure-theoretic) probability theory as it is treated in the course "Probability Theory" (401-3601-00L). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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