# Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Data Science Master | ||||||

Kernfächer | ||||||

Wählbare Kernfächer | ||||||

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

263-4400-00L | Advanced Graph Algorithms and Optimization Number of participants limited to 30. | W | 5 KP | 3G + 1A | R. Kyng | |

Kurzbeschreibung | This course will cover a number of advanced topics in optimization and graph algorithms. | |||||

Lernziel | The course will take students on a deep dive into modern approaches to graph algorithms using convex optimization techniques. By studying convex optimization through the lens of graph algorithms, students should develop a deeper understanding of fundamental phenomena in optimization. The course will cover some traditional discrete approaches to various graph problems, especially flow problems, and then contrast these approaches with modern, asymptotically faster methods based on combining convex optimization with spectral and combinatorial graph theory. | |||||

Inhalt | Students should leave the course understanding key concepts in optimization such as first and second-order optimization, convex duality, multiplicative weights and dual-based methods, acceleration, preconditioning, and non-Euclidean optimization. Students will also be familiarized with central techniques in the development of graph algorithms in the past 15 years, including graph decomposition techniques, sparsification, oblivious routing, and spectral and combinatorial preconditioning. | |||||

Voraussetzungen / Besonderes | This course is targeted toward masters and doctoral students with an interest in theoretical computer science. Students should be comfortable with design and analysis of algorithms, probability, and linear algebra. Having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, but not formally required. If you are not sure whether you're ready for this class or not, please consult the instructor. | |||||

263-5300-00L | Guarantees for Machine Learning | W | 5 KP | 2V + 2A | F. Yang | |

Kurzbeschreibung | This course teaches classical and recent methods in statistics and optimization commonly used to prove theoretical guarantees for machine learning algorithms. The knowledge is then applied in project work that focuses on understanding phenomena in modern machine learning. | |||||

Lernziel | This course is aimed at advanced master and doctorate students who want to understand and/or conduct independent research on theory for modern machine learning. For this purpose, students will learn common mathematical techniques from statistical learning theory. In independent project work, they then apply their knowledge and go through the process of critically questioning recently published work, finding relevant research questions and learning how to effectively present research ideas to a professional audience. | |||||

Inhalt | This course teaches some classical and recent methods in statistical learning theory aimed at proving theoretical guarantees for machine learning algorithms, including topics in - concentration bounds, uniform convergence - high-dimensional statistics (e.g. Lasso) - prediction error bounds for non-parametric statistics (e.g. in kernel spaces) - minimax lower bounds - regularization via optimization The project work focuses on active theoretical ML research that aims to understand modern phenomena in machine learning, including but not limited to - how overparameterization could help generalization ( interpolating models, linearized NN ) - how overparameterization could help optimization ( non-convex optimization, loss landscape ) - complexity measures and approximation theoretic properties of randomly initialized and trained NN - generalization of robust learning ( adversarial robustness, standard and robust error tradeoff ) - prediction with calibrated confidence ( conformal prediction, calibration ) | |||||

Voraussetzungen / Besonderes | It’s absolutely necessary for students to have a strong mathematical background (basic real analysis, probability theory, linear algebra) and good knowledge of core concepts in machine learning taught in courses such as “Introduction to Machine Learning”, “Regression”/ “Statistical Modelling”. It's also helpful to have heard an optimization course or approximation theoretic course. In addition to these prerequisites, this class requires a certain degree of mathematical maturity—including abstract thinking and the ability to understand and write proofs. | |||||

401-0674-00L | Numerical Methods for Partial Differential EquationsNicht 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 | 1 Second-Order Scalar Elliptic Boundary Value Problems 1.2 Equilibrium Models: Examples 1.3 Sobolev spaces 1.4 Linear Variational Problems 1.5 Equilibrium Models: Boundary Value Problems 1.6 Diffusion Models (Stationary Heat Conduction) 1.7 Boundary Conditions 1.8 Second-Order Elliptic Variational Problems 1.9 Essential and Natural Boundary Conditions 2 Finite Element Methods (FEM) 2.2 Principles of Galerkin Discretization 2.3 Case Study: Linear FEM for Two-Point Boundary Value Problems 2.4 Case Study: Triangular Linear FEM in Two Dimensions 2.5 Building Blocks of General Finite Element Methods 2.6 Lagrangian Finite Element Methods 2.7 Implementation of Finite Element Methods 2.7.1 Mesh Generation and Mesh File Format 2.7.2 Mesh Information and Mesh Data Structures 2.7.2.1 L EHR FEM++ Mesh: Container Layer 2.7.2.2 L EHR FEM++ Mesh: Topology Layer 2.7.2.3 L EHR FEM++ Mesh: Geometry Layer 2.7.3 Vectors and Matrices 2.7.4 Assembly Algorithms 2.7.4.1 Assembly: Localization 2.7.4.2 Assembly: Index Mappings 2.7.4.3 Distribute Assembly Schemes 2.7.4.4 Assembly: Linear Algebra Perspective 2.7.5 Local Computations 2.7.5.1 Analytic Formulas for Entries of Element Matrices 2.7.5.2 Local Quadrature 2.7.6 Treatment of Essential Boundary Conditions 2.8 Parametric Finite Element Methods 3 FEM: Convergence and Accuracy 3.1 Abstract Galerkin Error Estimates 3.2 Empirical (Asymptotic) Convergence of Lagrangian FEM 3.3 A Priori (Asymptotic) Finite Element Error Estimates 3.4 Elliptic Regularity Theory 3.5 Variational Crimes 3.6 FEM: Duality Techniques for Error Estimation 3.7 Discrete Maximum Principle 3.8 Validation and Debugging of Finite Element Codes 4 Beyond FEM: Alternative Discretizations [dropped] 5 Non-Linear Elliptic Boundary Value Problems [dropped] 6 Second-Order Linear Evolution Problems 6.1 Time-Dependent Boundary Value Problems 6.2 Parabolic Initial-Boundary Value Problems 6.3 Linear Wave Equations 7 Convection-Diffusion Problems [dropped] 8 Numerical Methods for Conservation Laws 8.1 Conservation Laws: Examples 8.2 Scalar Conservation Laws in 1D 8.3 Conservative Finite Volume (FV) Discretization 8.4 Timestepping for Finite-Volume Methods 8.5 Higher-Order Conservative Finite-Volume Schemes | |||||

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. | |||||

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. | |||||

401-3052-05L | Graph Theory | 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. | |||||

401-3052-10L | Graph Theory | W | 10 KP | 4V + 1U | B. Sudakov | |

Kurzbeschreibung | Basics, trees, Caley's formula, matrix tree theorem, connectivity, theorems of Mader and Menger, Eulerian graphs, Hamilton cycles, theorems of Dirac, Ore, Erdös-Chvatal, matchings, theorems of Hall, König, Tutte, planar graphs, Euler's formula, Kuratowski's theorem, graph colorings, Brooks' theorem, 5-colorings of planar graphs, list colorings, Vizing's theorem, Ramsey theory, Turán's theorem | |||||

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. | |||||

401-3602-00L | Applied Stochastic Processes Findet dieses Semester nicht statt. | W | 8 KP | 3V + 1U | keine Angaben | |

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). | |||||

401-4632-15L | Causality | W | 4 KP | 2G | C. Heinze-Deml | |

Kurzbeschreibung | In statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference. | |||||

Lernziel | After this course, you should be able to - understand the language and concepts of causal inference - know the assumptions under which one can infer causal relations from observational and/or interventional data - describe and apply different methods for causal structure learning - given data and a causal structure, derive causal effects and predictions of interventional experiments | |||||

Voraussetzungen / Besonderes | Prerequisites: basic knowledge of probability theory and regression | |||||

401-4944-20L | Mathematics of Data Science | W | 8 KP | 4G | A. Bandeira | |

Kurzbeschreibung | Mostly self-contained, but fast-paced, introductory masters level course on various theoretical aspects of algorithms that aim to extract information from data. | |||||

Lernziel | Introduction to various mathematical aspects of Data Science. | |||||

Inhalt | These topics lie in overlaps of (Applied) Mathematics with: Computer Science, Electrical Engineering, Statistics, and/or Operations Research. Each lecture will feature a couple of Mathematical Open Problem(s) related to Data Science. The main mathematical tools used will be Probability and Linear Algebra, and a basic familiarity with these subjects is required. There will also be some (although knowledge of these tools is not assumed) Graph Theory, Representation Theory, Applied Harmonic Analysis, among others. The topics treated will include Dimension reduction, Manifold learning, Sparse recovery, Random Matrices, Approximation Algorithms, Community detection in graphs, and several others. | |||||

Skript | https://people.math.ethz.ch/~abandeira/TenLecturesFortyTwoProblems.pdf | |||||

Voraussetzungen / Besonderes | The main mathematical tools used will be Probability, Linear Algebra (and real analysis), and a working knowledge of these subjects is required. In addition to these prerequisites, this class requires a certain degree of mathematical maturity--including abstract thinking and the ability to understand and write proofs. We encourage students who are interested in mathematical data science to take both this course and ``227-0434-10L Mathematics of Information'' taught by Prof. H. Bölcskei. The two courses are designed to be complementary. A. Bandeira and H. Bölcskei | |||||

401-6102-00L | Multivariate StatisticsFindet dieses Semester nicht statt. | W | 4 KP | 2G | keine Angaben | |

Kurzbeschreibung | Multivariate Statistics deals with joint distributions of several random variables. This course introduces the basic concepts and provides an overview over classical and modern methods of multivariate statistics. We will consider the theory behind the methods as well as their applications. | |||||

Lernziel | After the course, you should be able to: - describe the various methods and the concepts and theory behind them - identify adequate methods for a given statistical problem - use the statistical software "R" to efficiently apply these methods - interpret the output of these methods | |||||

Inhalt | Visualization / Principal component analysis / Multidimensional scaling / The multivariate Normal distribution / Factor analysis / Supervised learning / Cluster analysis | |||||

Skript | None | |||||

Literatur | The course will be based on class notes and books that are available electronically via the ETH library. | |||||

Voraussetzungen / Besonderes | Target audience: This course is the more theoretical version of "Applied Multivariate Statistics" (401-0102-00L) and is targeted at students with a math background. Prerequisite: A basic course in probability and statistics. Note: The courses 401-0102-00L and 401-6102-00L are mutually exclusive. You may register for at most one of these two course units. | |||||

402-0448-01L | Quantum Information Processing I: ConceptsDieser theoretisch ausgerichtete Teil QIP I bildet zusammen mit dem experimentell ausgerichteten Teil 402-0448-02L QIP II, die beide im Frühjahrssemester angeboten werden, im Master-Studiengang Physik das experimentelle Kernfach "Quantum Information Processing" mit total 10 ECTS-Kreditpunkten. | W | 5 KP | 2V + 1U | P. Kammerlander | |

Kurzbeschreibung | The course will cover the key concepts and ideas of quantum information processing, including descriptions of quantum algorithms which give the quantum computer the power to compute problems outside the reach of any classical supercomputer. Key concepts such as quantum error correction will be described. These ideas provide fundamental insights into the nature of quantum states and measurement. | |||||

Lernziel | We aim to provide an overview of the central concepts in Quantum Information Processing, including insights into the advantages to be gained from using quantum mechanics and the range of techniques based on quantum error correction which enable the elimination of noise. | |||||

Inhalt | The topics covered in the course will include quantum circuits, gate decomposition and universal sets of gates, efficiency of quantum circuits, quantum algorithms (Shor, Grover, Deutsch-Josza,..), error correction, fault-tolerant design, entanglement, teleportation and dense conding, teleportation of gates, and cryptography. | |||||

Skript | More details to follow. | |||||

Literatur | Quantum Computation and Quantum Information Michael Nielsen and Isaac Chuang Cambridge University Press | |||||

Voraussetzungen / Besonderes | Basic knowledge in the formalism of quantum states, unitary evolution and quantum measurement is recommended. | |||||

701-0104-00L | Statistical Modelling of Spatial Data | W | 3 KP | 2G | A. J. Papritz | |

Kurzbeschreibung | In environmental sciences one often deals with spatial data. When analysing such data the focus is either on exploring their structure (dependence on explanatory variables, autocorrelation) and/or on spatial prediction. The course provides an introduction to geostatistical methods that are useful for such analyses. | |||||

Lernziel | The course will provide an overview of the basic concepts and stochastic models that are used to model spatial data. In addition, participants will learn a number of geostatistical techniques and acquire familiarity with R software that is useful for analyzing spatial data. | |||||

Inhalt | After an introductory discussion of the types of problems and the kind of data that arise in environmental research, an introduction into linear geostatistics (models: stationary and intrinsic random processes, modelling large-scale spatial patterns by linear regression, modelling autocorrelation by variogram; kriging: mean square prediction of spatial data) will be taught. The lectures will be complemented by data analyses that the participants have to do themselves. | |||||

Skript | Slides, descriptions of the problems for the data analyses and solutions to them will be provided. | |||||

Literatur | P.J. Diggle & P.J. Ribeiro Jr. 2007. Model-based Geostatistics. Springer. Bivand, R. S., Pebesma, E. J. & Gómez-Rubio, V. 2013. Applied Spatial Data Analysis with R. Springer. | |||||

Voraussetzungen / Besonderes | Familiarity with linear regression analysis (e.g. equivalent to the first part of the course 401-0649-00L Applied Statistical Regression) and with the software R (e.g. 401-6215-00L Using R for Data Analysis and Graphics (Part I), 401-6217-00L Using R for Data Analysis and Graphics (Part II)) are required for attending the course. | |||||

Interdisziplinäre Wahlfächer | ||||||

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

101-0478-00L | Measurement and Modelling of Travel Behaviour | W | 6 KP | 4G | K. W. Axhausen | |

Kurzbeschreibung | Comprehensive introduction to survey methods in transport planning and modeling of travel behavior, using advanced discrete choice models. | |||||

Lernziel | Enabling the student to understand and apply the various measurement approaches and models of modelling travel behaviour. | |||||

Inhalt | Behavioral model and measurement; travel diary, design process, hypothetical markets, discrete choice model, parameter estimation, pattern of travel behaviour, market segments, simulation, advanced discrete choice models | |||||

Skript | Various papers and notes are distributed during the course. | |||||

103-0228-00L | Multimedia CartographyVoraussetzung: Erfolgreicher Abschluss der Lerneinheit Cartography III (103-0227-00L). | W | 4 KP | 3G | H.‑R. Bär, R. Sieber | |

Kurzbeschreibung | Focus of this course is on the realization of an atlas project in a small team. During the first part of the course, the necessary organizational, creative and technological basics will be provided. At the end of the course, the interactive atlas projects will be presented by the team members. | |||||

Lernziel | The goal of this course is to provide the students the theoretical background, knowledge and practical skills necessary to plan, design and create an interactive Web atlas based on modern Web technologies. | |||||

Inhalt | This course will cover the following topics: - Web map design - Project management - Graphical user interfaces in Web atlases - Interactions in map and atlas applications - Web standards - Programming interactive Web applications - Use of software libraries - Cartographic Web services - Code repository - Copyright and the Internet | |||||

Skript | Lecture notes and additional material are available on Moodle. | |||||

Literatur | - Cartwright, William; Peterson, Michael P. and Georg Gartner (2007); Multimedia Cartography, Springer, Heidelberg | |||||

Voraussetzungen / Besonderes | Prerequisites: Successful completion of Cartography III (103-0227-00L). Previous knowledge in Web programming. The students are expected to - present their work in progress on a regular basis - present their atlas project at the end of the course - keep records of all the work done - document all individual contributions to the project | |||||

103-0247-00L | Mobile GIS and Location-Based Services | W | 5 KP | 4G | P. Kiefer | |

Kurzbeschreibung | The course introduces students to the theoretical and technological background of mobile geographic information systems and location-based services. In lab sessions students acquire competences in mobile GIS design and implementation. | |||||

Lernziel | Students will - learn about the implications of mobility on GIS - get a detailed overview on research fields related to mobile GIS - get an overview on current mobile GIS and LBS technology, and learn how to assess new technologies in this fast-moving field - achieve an integrated view of Geospatial Web Services and mobile GIS - acquire competences in mobile GIS design and implementation | |||||

Inhalt | - LBS and mobile GIS: architectures, market, applications, and application development - Development for Android - Introduction to augmented reality development (HoloLens) - Mobile decision-making, context, personalization, and privacy - Mobile human computer interaction and user interfaces - Mobile behavior interpretation | |||||

Voraussetzungen / Besonderes | Elementary programming skills (Java) | |||||

103-0255-01L | Geodatenanalyse | W | 2 KP | 2G | K. Kurzhals | |

Kurzbeschreibung | Die Lehrveranstaltung behandelt weiterführende Methoden der Geodatenanalyse. | |||||

Lernziel | - Verstehen der theoretischen Grundlagen räumlicher Analyseverfahren. - Verstehen und Anwenden von Methoden zur raumbezogenen Datenanalyse. - Erkennen häufiger Fehlerquellen bei der Geodatenanalyse. - Vertiefende praktische Kenntnisse in der Anwendung entsprechender GIS-Tools. - Erlernen der Fähigkeit, raumbezogene Probleme jenseits klassischer GIS Software zu lösen. | |||||

Inhalt | In der Lehrveranstaltung werden weiterführende Methoden räumlicher Analyseverfahren theoretisch behandelt sowie anhand von Übungsaufgaben angewendet. | |||||

Skript | kein Skript. | |||||

Literatur | Eine Literaturliste wird in der Lehrveranstaltung zur Verfügung gestellt. | |||||

Voraussetzungen / Besonderes | Voraussetzungen: Basiswissen im Bereich der Geoinformationstechnologien und der Verwendung von Geoinformationssystemen entsprechend den Vorlesungen GIS I und GIS II im Bachelor-Studiengang Geomatik und Planung. | |||||

227-0945-10L | Cell and Molecular Biology for Engineers IIThis course is part II of a two-semester course. Knowledge of part I is required. | 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: DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development 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, Morgan, Raff, Roberts, and Walter. | |||||

227-0391-00L | Medical Image AnalysisBasic knowledge of computer vision would be helpful. | W | 3 KP | 2G | E. Konukoglu, M. A. Reyes Aguirre | |

Kurzbeschreibung | It is the objective of this lecture to introduce the basic concepts used in Medical Image Analysis. In particular the lecture focuses on shape representation schemes, segmentation techniques, machine learning based predictive models and various image registration methods commonly used in Medical Image Analysis applications. | |||||

Lernziel | This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas. | |||||

Voraussetzungen / Besonderes | Prerequisites: Basic concepts of mathematical analysis and linear algebra. Preferred: Basic knowledge of computer vision and machine learning would be helpful. The course will be held in English. | |||||

261-5113-00L | Computational Challenges in Medical Genomics Number of participants limited to 20. | W | 2 KP | 2S | A. Kahles, G. Rätsch | |

Kurzbeschreibung | This seminar discusses recent relevant contributions to the fields of computational genomics, algorithmic bioinformatics, statistical genetics and related areas. Each participant will hold a presentation and lead the subsequent discussion. | |||||

Lernziel | Preparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to succesfully present the key points of existing research work are the same as needed to communicate own research ideas. In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class. | |||||

Inhalt | The topics covered in the seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc. Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are amongst the list of selected papers. | |||||

Voraussetzungen / Besonderes | Knowledge of algorithms and data structures and interest in applications in genomics and computational biomedicine. | |||||

261-5120-00L | Machine Learning for Health Care Number of participants limited to 150. | W | 5 KP | 3P + 1A | G. Rätsch, J. Vogt, V. Boeva | |

Kurzbeschreibung | The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems. | |||||

Lernziel | During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in biomedicine, discuss the main challenges they present and their current technical solutions. | |||||

Inhalt | The course will consist of four topic clusters that will cover the most relevant applications of ML in Biomedicine: 1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc. 2) Medical notes: Vast amount of medical observations are stored in the form of free text, we will analyze stategies for extracting knowledge from them. 3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them. 4) Genomics data: ML in genomics is still an emerging subfield, but given that genomics data are arguably the most extensive and complex datasets that can be found in biomedicine, it is expected that many relevant ML applications will arise in the near future. We will review and discuss current applications and challenges. | |||||

Voraussetzungen / Besonderes | Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line Relation to Course 261-5100-00 Computational Biomedicine: This course is a continuation of the previous course with new topics related to medical data and machine learning. The format of Computational Biomedicine II will also be different. It is helpful but not essential to attend Computational Biomedicine before attending Computational Biomedicine II. | |||||

262-0200-00L | Bayesian Phylodynamics | W | 4 KP | 2G + 2A | T. Stadler, T. Vaughan | |

Kurzbeschreibung | How fast was Ebola spreading in West Africa? Where and when did the epidemic outbreak start? How can we construct the phylogenetic tree of great apes, and did gene flow occur between different apes? At the end of the course, students will have designed, performed, presented, and discussed their own phylodynamic data analysis to answer such questions. | |||||

Lernziel | Attendees will extend their knowledge of Bayesian phylodynamics obtained in the “Computational Biology” class (636-0017-00L) and will learn how to apply this theory to real world data. The main theoretical concepts introduced are: * Bayesian statistics * Phylogenetic and phylodynamic models * Markov Chain Monte Carlo methods Attendees will apply these concepts to a number of applications yielding biological insight into: * Epidemiology * Pathogen evolution * Macroevolution of species | |||||

Inhalt | In the first part of the semester, in each week, we will first present the theoretical concepts of Bayesian phylodynamics. The presentation will be followed by attendees using the software package BEAST v2 to apply these theoretical concepts to empirical data. We use previously published datasets on e.g. Ebola, Zika, Yellow Fever, Apes, and Penguins for analysis. Examples of these practical tutorials are available on https://taming-the-beast.org/. In the second part of the semester, the students choose an empirical dataset of genetic sequencing data and possibly some non-genetic metadata. They then design and conduct a research project in which they perform Bayesian phylogenetic analyses of their dataset. The weekly class is intended to discuss and monitor progress and to address students’ questions very interactively. At the end of the semester, the students present their research project in an oral presentation. The content of the presentation, the style of the presentation, and the performance in answering the questions after the presentation will be marked. | |||||

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

Literatur | The following books provide excellent background material: • Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST. • Yang, Z. 2014. Molecular Evolution: A Statistical Approach. • Felsenstein, J. 2003. Inferring Phylogenies. The tutorials in this course are based on our Summer School “Taming the BEAST”: https://taming-the-beast.org/ | |||||

Voraussetzungen / Besonderes | This class builds upon the content which we teach in the Computational Biology class (636-0017-00L). Attendees must have either taken the Computational Biology class or acquired the content elsewhere. |

- Seite 2 von 4 Alle