Search result: Catalogue data in Autumn Semester 2021
Computational Biology and Bioinformatics Master  More information at: https://www.cbb.ethz.ch/ | |||||||||||||||||||||||||||
 Core CoursesPlease 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 | |||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||
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| 636-0009-00L | Evolutionary Dynamics | W | 6 credits | 2V + 1U + 2A | N. Beerenwinkel | ||||||||||||||||||||||
| Abstract | Evolutionary dynamics is concerned with the mathematical principles according to which life has evolved. This course offers an introduction to mathematical modeling of evolution, including deterministic and stochastic models, with an emphasis on tumor evolution. | ||||||||||||||||||||||||||
| Learning objective | The goal of this course is to understand and to appreciate mathematical models and computational methods that provide insight into the evolutionary process in general and tumor evolution in particular. Students should analyze and evaluate models and their application critically and be able to design new models. | ||||||||||||||||||||||||||
| Content | Evolution is the one theory that encompasses all of biology. It provides a single, unifying concept to understand the living systems that we observe today. We will introduce several types of mathematical models of evolution to describe gene frequency changes over time in the context of different biological systems, focusing on asexual populations. Viruses and cancer cells provide the most prominent examples of such systems and they are at the same time of great biomedical interest. The course will cover some classical mathematical population genetics and population dynamics, and also introduce several new approaches. This is reflected in a diverse set of mathematical concepts which make their appearance throughout the course, all of which are introduced from scratch. Topics covered include the quasispecies equation, evolution of HIV, evolutionary game theory, evolutionary stability, evolutionary graph theory, tumor evolution, stochastic tunneling, genetic progression of cancer, diffusion theory, fitness landscapes, branching processes, and evolutionary escape. | ||||||||||||||||||||||||||
| Lecture notes | No. | ||||||||||||||||||||||||||
| Literature | - Evolutionary Dynamics. Martin A. Nowak. The Belknap Press of Harvard University Press, 2006. - Evolutionary Theory: Mathematical and Conceptual Foundations. Sean H. Rice. Sinauer Associates, Inc., 2004. | ||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Basic mathematics (linear algebra, calculus, probability) | ||||||||||||||||||||||||||
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| 636-0017-00L | Computational Biology | W | 6 credits | 3G + 2A | T. Vaughan | ||||||||||||||||||||||
| Abstract | 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. | ||||||||||||||||||||||||||
| Learning objective | 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 | ||||||||||||||||||||||||||
| Content | 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. | ||||||||||||||||||||||||||
| Lecture notes | Lecture slides will be available on moodle. | ||||||||||||||||||||||||||
| Literature | 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. | ||||||||||||||||||||||||||
| Prerequisites / Notice | 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. | ||||||||||||||||||||||||||
| 262-6100-00L | Evolutionary Genetics | W | 4 credits | 3G | external organisers | ||||||||||||||||||||||
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| 262-6110-00L | Bioinformatics Algorithms | W | 4 credits | 3G | external organisers | ||||||||||||||||||||||
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| 401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student. UZH Module Code: STA426 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | W | 5 credits | 3G | H. Rehrauer, M. Robinson | ||||||||||||||||||||||
| Abstract | A range of topics will be covered, including basic molecular biology, genomics technologies and in particular, a wide range of statistical and computational methods that have been used in the analysis of DNA microarray and high throughput sequencing experiments. | ||||||||||||||||||||||||||
| Learning objective | -Understand the fundamental "scientific process" in the field of Statistical Bioinformatics -Be equipped with the skills/tools to preprocess genomic data (Unix, Bioconductor, mapping, etc.) and ensure reproducible research (Sweave) -Have a general knowledge of the types of data and biological applications encountered with microarray and sequencing data -Have the general knowledge of the range of statistical methods that get used with microarray and sequencing data -Gain the ability to apply statistical methods/knowledge/software to a collaborative biological project -Gain the ability to critical assess the statistical bioinformatics literature -Write a coherent summary of a bioinformatics problem and its solution in statistical terms | ||||||||||||||||||||||||||
| Content | Lectures will include: microarray preprocessing; normalization; exploratory data analysis techniques such as clustering, PCA and multidimensional scaling; Controlling error rates of statistical tests (FPR versus FDR versus FWER); limma (linear models for microarray analysis); mapping algorithms (for RNA/ChIP-seq); RNA-seq quantification; statistical analyses for differential count data; isoform switching; epigenomics data including DNA methylation; gene set analyses; classification | ||||||||||||||||||||||||||
| Lecture notes | Lecture notes, published manuscripts | ||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Basic knowlegde of the programming language R, sufficient knowledge in statistics Former course title: Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data | ||||||||||||||||||||||||||
| Biophysics | |||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||
| 262-6106-00L | Current Topics in Biophysics | W | 6 credits | 3G | external organisers | ||||||||||||||||||||||
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| 636-0104-00L | Biophysical Methods | W | 4 credits | 3G | D. J. Müller | ||||||||||||||||||||||
| Abstract | Students will be imparted knowledge in basic and advanced biophysical methods applied to problems in molecular biotechnology. The course is fundamental to applying the methods in their daily and advanced research routines. The students will learn the physical basis of the methods as well as their limitations and possibilities to address existing and future topics in molecular biotechnology. | ||||||||||||||||||||||||||
| Learning objective | Gain of interdisciplinary competence in experimental and theoretical research, which qualifies for academic scientific work (master's or doctoral thesis) as well as for research in a biotechnology or a pharmaceutical company. The module is of general use in courses focused on modern biomolecular technologies, systems biology and systems engineering. | ||||||||||||||||||||||||||
| Content | The students will learn basic and advanced knowledge in applying biophysical methods to address problems and overcome challenges in biotechnology, cell biology and life sciences in general. The biological and physical possibilities and limitations of the methods will be discussed and critically evaluated. By the end of the course the students will have assimilated knowledge on a portfolio of biophysical tools widening their research capabilities and aptitude. The biophysical methods to be taught will include: • Light microscopy: Resolution limit of light microscopy, fluorescence, GFP, fluorescence microscopy, DIC, phase contrast, difference between wide-field and confocal microscopy • Super resolution optical microscopy: STED, PALM, STORM, other variations • Electron microscopy: Scanning electron microscopy, transmission electron microscopy, electron tomography, cryo-electron microscopy, single particle analysis and averaging, tomography, sectioning, negative stain • X-ray, electron and neutron diffraction • MRI Imaging • Scanning tunnelling microscopy and atomic force microscopy • Patch clamp technologies: Principles of patch clamp analysis and application. Various patch clamp approaches used in research and industry • Surface plasmon resonance-based biosensors • Molecular pore-based sensors and sequencing devices • Mechanical molecular and cellular assembly devices • Optical and magnetic tweezers • CD spectroscopy • Optogenetics • Molecular dynamics simulations | ||||||||||||||||||||||||||
| Lecture notes | Hand out will be given to students at lecture. | ||||||||||||||||||||||||||
| Literature | Methods in Molecular Biophysics (5th edition), Serdyuk et al., Cambridge University Press Biochemistry (5th edition), Berg, Tymoczko, Stryer; ISBN 0-7167-4684-0, Freeman Bioanalytics, Lottspeich & Engels, Wiley VCH, ISBN-10: 3527339191 Cell Biology, Pollard & Earnshaw; ISBN:0-7216-3997-6, Saunder, Pennsylvania Methods in Modern Biophysics, Nölting, 3rd Edition, Springer, ISBN-10: 3642030211 | ||||||||||||||||||||||||||
| Prerequisites / Notice | The module is composed of 3 SWS (3 hours/week): 2-hour lecture, 1-hour seminar. For the seminar, students will prepare oral presentations on specific in-depth subjects with/under the guidance of the teacher. | ||||||||||||||||||||||||||
| 529-0004-01L | Classical Simulation of (Bio)Molecular Systems | W | 6 credits | 4G | P. H. Hünenberger, J. Dolenc, S. Riniker | ||||||||||||||||||||||
| Abstract | Molecular models, classical force fields, configuration sampling, molecular dynamics simulation, boundary conditions, electrostatic interactions, analysis of trajectories, free-energy calculations, structure refinement, applications in chemistry and biology. Exercises: hands-on computer exercises for learning progressively how to perform an analyze classical simulations (using the package GROMOS). | ||||||||||||||||||||||||||
| Learning objective | Introduction to classical (atomistic) computer simulation of (bio)molecular systems, development of skills to carry out and interpret these simulations. | ||||||||||||||||||||||||||
| Content | Molecular models, classical force fields, configuration sampling, molecular dynamics simulation, boundary conditions, electrostatic interactions, analysis of trajectories, free-energy calculations, structure refinement, applications in chemistry and biology. Exercises: hands-on computer exercises for learning progressively how to perform an analyze classical simulations (using the package GROMOS). | ||||||||||||||||||||||||||
| Lecture notes | The powerpoint slides of the lectures will be made available weekly on the website in pdf format (on the day preceding each lecture). | ||||||||||||||||||||||||||
| Literature | See: www.csms.ethz.ch/education/CSBMS | ||||||||||||||||||||||||||
| Prerequisites / Notice | Since the exercises on the computer do convey and test essentially different skills than those being conveyed during the lectures and tested at the oral exam, the results of the exercises are taken into account when evaluating the results of the exam (learning component, possible bonus of up to 0.25 points on the exam mark). For more information about the lecture: www.csms.ethz.ch/education/CSBMS | ||||||||||||||||||||||||||
| Biosystems | |||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||
| 636-0007-00L | Computational Systems Biology | W | 6 credits | 3V + 2U | J. Stelling | ||||||||||||||||||||||
| Abstract | Study of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification). | ||||||||||||||||||||||||||
| Learning objective | The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks. | ||||||||||||||||||||||||||
| Content | Biology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods. | ||||||||||||||||||||||||||
| Lecture notes | http://www.csb.ethz.ch/education/lectures.html | ||||||||||||||||||||||||||
| Literature | U. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006. Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2010. B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013 | ||||||||||||||||||||||||||
| 636-0706-00L | Spatio-Temporal Modelling in Biology | W | 4 credits | 3G | D. Iber | ||||||||||||||||||||||
| Abstract | This course focuses on modeling spatio-temporal problems in biology, in particular on the cell and tissue level. The main focus is on mechanisms and concepts, but mathematical and numerical techniques are introduced as required. Biological examples discussed in the course provide an introduction to key concepts in developmental biology. | ||||||||||||||||||||||||||
| Learning objective | Students will learn state-of-the-art approaches to modelling spatial effects in dynamical biological systems. The course provides an introduction to dynamical system, and covers the mathematical analysis of pattern formation in growing, developing systems, as well as the description of mechanical effects at the cell and tissue level. The course also provides an introduction to image-based modelling, i.e. the use of microscopy data for model development and testing. The course covers classic as well as current approaches and exposes students to open problems in the field. In this way, the course seeks to prepare students to conduct research in the field. The course prepares students for research in developmental biology, as well as for applications in tissue engineering, and for biomedical research. | ||||||||||||||||||||||||||
| Content | 1. Introduction to Modelling in Biology 2. Morphogen Gradients 3. Dynamical Systems 4. Cell-cell Signalling (Dr Boareto) 5. Travelling Waves 6. Turing Patterns 7. Chemotaxis 8. Mathematical Description of Growing Biological Systems 9. Image-Based Modelling 10. Tissue Mechanics 11. Cell-based Tissue Simulation Frameworks 12. Plant Development (Dr Dumont) 13. Growth Control 14. Summary | ||||||||||||||||||||||||||
| Lecture notes | All lecture material will be made available online Link | ||||||||||||||||||||||||||
| Literature | The lecture course is not based on any textbook. The following textbooks are related to some of its content. The textbooks may be of interest for further reading, but are not necessary to follow the course: Murray, Mathematical Biology, Springer Forgacs and Newman, Biological Physics of the Developing Embryo, CUP Keener and Sneyd, Mathematical Physiology, Springer Fall et al, Computational Cell Biology, Springer Szallasi et al, System Modeling in Cellular Biology, MIT Press Wolkenhauer, Systems Biology Kreyszig, Engineering Mathematics, Wiley | ||||||||||||||||||||||||||
| Prerequisites / Notice | The course is self-contained. The course assumes no background in biology but a good foundation regarding mathematical and computational techniques. | ||||||||||||||||||||||||||
| 636-0117-00L | Mathematical Modelling for Bioengineering and Systems Biology | W | 4 credits | 3G | D. Iber | ||||||||||||||||||||||
| Abstract | Basic concepts and mathematical tools to explore biochemical reaction kinetics and biological network dynamics. | ||||||||||||||||||||||||||
| Learning objective | The course enables students to formulate, analyse, and simulate mathematical models of biochemical networks. To this end, the course covers basic mathematical concepts and tools to explore biochemical reaction dynamics as well as basic concepts from dynamical systems theory. The exercises serve to deepen the understanding of the presented concepts and the mathematical methods, and to train students to numerically solve and simulate mathematical models. | ||||||||||||||||||||||||||
| Content | Biochemical Reaction Modelling Basic Concepts from Linear Algebra & Differential Equations Mathematical Methods: Linear Stability Analysis, Phase Plane Analysis, Bifurcation Analysis Dynamical Systems: Switches, Oscillators, Adaptation Signal Propagation in Signalling Networks Parameter Estimation | ||||||||||||||||||||||||||
| Data Science | |||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||
| 636-0018-00L | Data Mining I | W | 6 credits | 3G + 2A | K. M. Borgwardt | ||||||||||||||||||||||
| Abstract | Data Mining, the search for statistical dependencies in large databases, is of utmost important in modern society, in particular in biological and medical research. This course provides an introduction to the key problems, concepts, and algorithms in data mining, and the applications of data mining in computational biology. | ||||||||||||||||||||||||||
| Learning objective | The goal of this course is that the participants gain an understanding of data mining problems and algorithms to solve these problems, in particular in biological and medical applications. | ||||||||||||||||||||||||||
| Content | The goal of the field of data mining is to find patterns and statistical dependencies in large databases, to gain an understanding of the underlying system from which the data were obtained. In computational biology, data mining contributes to the analysis of vast experimental data generated by high-throughput technologies, and thereby enables the generation of new hypotheses. In this course, we will present the algorithmic foundations of data mining and its applications in computational biology. The course will feature an introduction to popular data mining problems and algorithms, reaching from classification via clustering to feature selection. This course is intended for both students who are interested in applying data mining algorithms and students who would like to gain an understanding of the key algorithmic concepts in data mining. Tentative list of topics: 1. Distance functions 2. Classification 3. Clustering 4. Feature Selection | ||||||||||||||||||||||||||
| Lecture notes | Course material will be provided in form of slides. | ||||||||||||||||||||||||||
| Literature | Will be provided during the course. | ||||||||||||||||||||||||||
| Prerequisites / Notice | Basic understanding of mathematics, as taught in basic mathematics courses at the Bachelor's level. | ||||||||||||||||||||||||||
| 252-0535-00L | Advanced Machine Learning | W | 10 credits | 3V + 2U + 4A | J. M. Buhmann, C. Cotrini Jimenez | ||||||||||||||||||||||
| Abstract | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | ||||||||||||||||||||||||||
| Learning objective | Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data. | ||||||||||||||||||||||||||
| Content | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems | ||||||||||||||||||||||||||
| Lecture notes | No lecture notes, but slides will be made available on the course webpage. | ||||||||||||||||||||||||||
| Literature | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | ||||||||||||||||||||||||||
| Prerequisites / Notice | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points. | ||||||||||||||||||||||||||
| Seminar Compulsory seminar. | |||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||
| 636-0704-00L | Computational Biology and Bioinformatics Seminar  Number of participants limited to 30 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 credits | 2S | N. Beerenwinkel, K. M. Borgwardt, D. Iber, M. H. Khammash, J. Stelling | ||||||||||||||||||||||
| Abstract | Computational biology and bioinformatics aim at an understanding of living systems through computation. The seminar combines student presentations and current research project presentations to review the rapidly developing field from a computer science perspective. Areas: DNA sequence analysis, proteomics, optimization and bio-inspired computing, and systems modeling, simulation and analysis. | ||||||||||||||||||||||||||
| Learning objective | 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. | ||||||||||||||||||||||||||
| Content | 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, University of Basel and University of Zurich, 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. | ||||||||||||||||||||||||||
| Literature | Original papers to be presented by the students will be provided in the first week of the seminar. | ||||||||||||||||||||||||||
| Advanced Courses A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof at least 16 ECTS in the Theory and at least 10 ECTS in the Biology category. Note that some of the lectures are being recorded: https://video.ethz.ch/lectures.html | |||||||||||||||||||||||||||
| Theory At least 16 ECTS need to be acquired in this category. | |||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||
| 401-0663-00L | Numerical Methods for Computer Science | W | 7 credits | 2V + 2U + 2P | R. Hiptmair | ||||||||||||||||||||||
| Abstract | The course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++. | ||||||||||||||||||||||||||
| Learning objective | * Knowledge of the fundamental algorithms in numerical mathematics * Knowledge of the essential terms in numerical mathematics and the techniques used for the analysis of numerical algorithms * Ability to choose the appropriate numerical method for concrete problems * Ability to interpret numerical results * Ability to implement numerical algorithms afficiently | ||||||||||||||||||||||||||
| Content | * Computing with Matrices and Vectors 2.1 Fundamentals 2.2 Software and Libraries 2.4 Computational Effort 2.5 Machine Arithmetic and Consequences * Direct Methods for (Square) Linear Systems of Equations 3.1 Introduction: Linear Systems of Equations (LSE) 3.2 Theory: Linear Systems of Equations (LSE) 3.5 Survey: Elimination Solvers for Linear Systems of Equations 3.7 Sparse Linear Systems * Direct Methods for Linear Least Squares Problems 4.1 Least Squares Solution Concepts 4.2 Normal Equation Methods 4.3 Orthogonal Transformation Methods 4.3.1 Transformation Idea 4.3.2 Orthogonal/Unitary Matrices 4.3.3 QR-Decomposition 4.3.4 QR-Based Solver for Linear Least Squares Problems 4.4 Singular Value Decomposition (SVD) 4.5 SVD-Based Optimization and Approximation * Filtering Algorithms 5.1 Filters and Convolutions 5.2 Discrete Fourier Transform (DFT) 5.3 Fast Fourier Transform (FFT) * Machine Learning of One-Dimensional Data (Data Interpolation and Data Fitting in 1D) 6.1 Abstract Interpolation (AI) 6.2 Global Polynomial Interpolation 6.4 Splines 6.7 Least Squares Data Fitting * Iterative Methods for Non-Linear Systems of Equations 9.2 Iterative Methods 9.4 Finding Zeros of Scalar Functions 9.5 Newton's Method in Rn 9.7 Non-linear Least Squares | ||||||||||||||||||||||||||
| Lecture notes | Lecture materials (PDF documents and codes) will be made available to the participants through the course web page and online repositories. Access information will be communicated in the beginning of the course. | ||||||||||||||||||||||||||
| Literature | U. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011. A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000. W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006. W. Gander, M.J. Gander, and F. Kwok "Scientific Computing", Springer 2014. M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002 P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002 | ||||||||||||||||||||||||||
| Prerequisites / Notice | The course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Familiarity with C++, object oriented and generic programming is an advantage. Participants of the course are expected to learn C++ by themselves, in case they do not know it already. | ||||||||||||||||||||||||||
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| 263-5210-00L | Probabilistic Artificial Intelligence | W | 8 credits | 3V + 2U + 2A | A. Krause | ||||||||||||||||||||||
| Abstract | This course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics. | ||||||||||||||||||||||||||
| Learning objective | How can we build systems that perform well in uncertain environments? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as robotics. The course is designed for graduate students. | ||||||||||||||||||||||||||
| Content | Topics covered: - Probability - Probabilistic inference (variational inference, MCMC) - Bayesian learning (Gaussian processes, Bayesian deep learning) - Probabilistic planning (MDPs, POMPDPs) - Multi-armed bandits and Bayesian optimization - Reinforcement learning | ||||||||||||||||||||||||||
| Prerequisites / Notice | Solid basic knowledge in statistics, algorithms and programming. The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite. | ||||||||||||||||||||||||||
| 401-0647-00L | Introduction to Mathematical Optimization | W | 5 credits | 2V + 1U | D. Adjiashvili | ||||||||||||||||||||||
| Abstract | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | ||||||||||||||||||||||||||
| Learning objective | The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering. | ||||||||||||||||||||||||||
| Content | Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering. | ||||||||||||||||||||||||||
| Literature | Information about relevant literature will be given in the lecture. | ||||||||||||||||||||||||||
| Prerequisites / Notice | This course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications. | ||||||||||||||||||||||||||
| 227-0225-00L | Linear System Theory | W | 6 credits | 5G | A. Iannelli | ||||||||||||||||||||||
| Abstract | The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems. | ||||||||||||||||||||||||||
| Learning objective | Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems. | ||||||||||||||||||||||||||
| Content | - Proof techniques and practices. - Linear spaces, normed linear spaces and Hilbert spaces. - Ordinary differential equations, existence and uniqueness of solutions. - Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case. - Controllability and observability, duality. Time invariant systems treated as a special case. - Stability and stabilization, observers, state and output feedback, separation principle. | ||||||||||||||||||||||||||
| Lecture notes | Available on the course Moodle platform. | ||||||||||||||||||||||||||
| Prerequisites / Notice | Sufficient mathematical maturity, in particular in linear algebra, analysis. | ||||||||||||||||||||||||||
| Competencies |
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| 151-0575-01L | Signals and Systems | W | 4 credits | 2V + 2U | A. Carron | ||||||||||||||||||||||
| Abstract | Signals arise in most engineering applications. They contain information about the behavior of physical systems. Systems respond to signals and produce other signals. In this course, we explore how signals can be represented and manipulated, and their effects on systems. We further explore how we can discover basic system properties by exciting a system with various types of signals. | ||||||||||||||||||||||||||
| Learning objective | Master the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise. | ||||||||||||||||||||||||||
| Content | Discrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design. | ||||||||||||||||||||||||||
| Lecture notes | Lecture notes available on course website. | ||||||||||||||||||||||||||
| Prerequisites / Notice | Control Systems I is helpful but not required. | ||||||||||||||||||||||||||
| 252-0237-00L | Concepts of Object-Oriented Programming | W | 8 credits | 3V + 2U + 2A | P. Müller | ||||||||||||||||||||||
| Abstract | Course that focuses on an in-depth understanding of object-oriented programming and compares designs of object-oriented programming languages. Topics include different flavors of type systems, inheritance models, encapsulation in the presence of aliasing, object and class initialization, program correctness, reflection | ||||||||||||||||||||||||||
| Learning objective | After this course, students will: Have a deep understanding of advanced concepts of object-oriented programming and their support through various language features. Be able to understand language concepts on a semantic level and be able to compare and evaluate language designs. Be able to learn new languages more rapidly. Be aware of many subtle problems of object-oriented programming and know how to avoid them. | ||||||||||||||||||||||||||
| Content | The main goal of this course is to convey a deep understanding of the key concepts of sequential object-oriented programming and their support in different programming languages. This is achieved by studying how important challenges are addressed through language features and programming idioms. In particular, the course discusses alternative language designs by contrasting solutions in languages such as C++, C#, Eiffel, Java, Python, and Scala. The course also introduces novel ideas from research languages that may influence the design of future mainstream languages. The topics discussed in the course include among others: The pros and cons of different flavors of type systems (for instance, static vs. dynamic typing, nominal vs. structural, syntactic vs. behavioral typing) The key problems of single and multiple inheritance and how different languages address them Generic type systems, in particular, Java generics, C# generics, and C++ templates The situations in which object-oriented programming does not provide encapsulation, and how to avoid them The pitfalls of object initialization, exemplified by a research type system that prevents null pointer dereferencing How to maintain the consistency of data structures | ||||||||||||||||||||||||||
| Literature | Will be announced in the lecture. | ||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Mastering at least one object-oriented programming language (this course will NOT provide an introduction to object-oriented programming); programming experience | ||||||||||||||||||||||||||
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