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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-6140-00L | Random Processes: Theory and Applications from Physics to Finance | W | 4 credits | 3G | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 262-6150-00L | Programming for Life Sciences | W | 4 credits | 2P | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 636-0015-00L | An Introduction to Probability Theory and Stochastic Processes with Applications to Biology | W | 4 credits | 3G | A. Gupta | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Biology is becoming increasingly quantitative and mathematical modeling is now an integral part of biological research. In many biological processes, ranging from gene-expression to evolution, randomness plays an important role that can only be understood using stochastic models. This course will provide the students with a theoretical foundation for developing such stochastic models and analyzing | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The aim of this course is to introduce certain topics in Probability Theory and Stochastic Processes that have been specifically selected with an eye on biological applications. This course will teach students the tools and techniques for modeling and analyzing random phenomena. Throughout the course, several biological applications will be discussed and students will be encouraged to do additional reading based on their research interests. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The first half of the course will cover the basics of Probability Theory while the second half will delve into the theory of Stochastic Processes. Below is the list of topics that will be covered in the course. 1. The mathematical representation of random phenomena: The probability space, properties of the probability measure, Independence of events, Conditional probability and Bayes formula, applications to parameter inference. 2. Random Variables and their distributions: Discrete and continuous random variables, Expectation and Variance, Important Examples of Random Variables, Independent random variables and their sums, Conditional Distribution and Conditional Expectation, Markov and Chebyshev inequalities. Law of total variation, estimation of intrinsic and extrinsic noise in biological systems. 3. Convergence of Random Variables: Modes of convergence, Laws of large numbers, the central limit theorem, the law of the iterated logarithm, Applications to the analysis of cell population data. 4. Generating functions and their applications: Definition and important examples, Random Walks, Branching processes, Coalescent processes, Modeling epidemic processes and stem-cell differentiation. 5. Markov chains: Transition functions and related computations, Classification of states and classification of chains. Concepts of recurrence, transience, irreducibility and periodicity, Stationary distributions, Continuous time Markov Chain model of a biochemical reaction network. 6. Stochastic Processes: Existence and Construction, Stationary Processes, Renewal Processes, The Wiener Process, The Ergodic Theorem, Leveraging experimental techniques in Biology. 7. Introduction to the theory of Martingales: Basic definitions, Martingale differences and Hoeffding's inequality, Martingale Convergence Theorem, Crossings and convergence, Stopping times and the optional sampling theorem, Doob's maximal inequalities, Applications to the analysis of stochastic biochemical reaction networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | While no specific textbook will be followed, much of the material and homework problems will be taken from the following books: An Introduction to Stochastic Processes with Applications to Biology, Linda Allen, Second Edition, Chapman and Hall, 2010. Probability And Random Processes, Grimmett and Stirzaker, Third Edition, Oxford University Press, 2001. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course will involve a healthy balance between mathematical rigor (theorem proving) and biological applications. Students are expected to have a good grasp of Linear Algebra and Multivariable Calculus. Basic knowledge of set theory will also be needed. Students should be prepared for abstract reasoning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-3010-00L | Big Data | W | 10 credits | 3V + 2U + 4A | G. Fourny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This combination of requirements, together with the technologies that have emerged in order to address them, is typically referred to as "Big Data." This revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm". Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it were and are still needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small. The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof. After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each business use case efficiently and consistently. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe. We take the monolithic, one-machine relational stack from the 1970s, smash it down and rebuild it on top of large clusters: starting with distributed storage, and all the way up to syntax, models, validation, processing, indexing, and querying. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem. No data is harmed during this course, however, please be psychologically prepared that our data may not always be in third normal form. - physical storage: distributed file systems (HDFS), object storage(S3), key-value stores - logical storage: document stores (MongoDB), column stores (HBase), graph databases (neo4j), data warehouses (ROLAP) - data formats and syntaxes (XML, JSON, RDF, Turtle, CSV, XBRL, YAML, protocol buffers, Avro) - data shapes and models (tables, trees, graphs, cubes) - type systems and schemas: atomic types, structured types (arrays, maps), set-based type systems (?, *, +) - an overview of functional, declarative programming languages across data shapes (SQL, XQuery, JSONiq, Cypher, MDX) - the most important query paradigms (selection, projection, joining, grouping, ordering, windowing) - paradigms for parallel processing, two-stage (MapReduce) and DAG-based (Spark) - resource management (YARN) - what a data center is made of and why it matters (racks, nodes, ...) - underlying architectures (internal machinery of HDFS, HBase, Spark, neo4j) - optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing) - applications. Large scale analytics and machine learning are outside of the scope of this course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Papers from scientific conferences and journals. References will be given as part of the course material during the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This course, in the autumn semester, is only intended for: - Computer Science students - Data Science students - CBB students with a Computer Science background Mobility students in CS are also welcome and encouraged to attend. If you experience any issue while registering, please contact the study administration and you will be gladly added. For students of all other departements interested in this fascinating topic: I would love to have you visit my lectures as well! So there is a series of two courses specially designed for you: - "Information Systems for Engineers" (SQL, relational databases): this Fall - "Big Data for Engineers" (similar to Big Data, but adapted for non Computer Scientists): Spring 2021 There is no hard dependency, so you can either them in any order, but it may be more enjoyable to start with Information Systems for Engineers. Students who successfully completed Big Data for Engineers are not allowed to enrol in the course Big Data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 261-5112-00L | Algorithms and Data Structures for Population Scale Genomics Does not take place this semester. Number of participants limited to 30. | W | 3 credits | 2G | to be announced | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Research in Biology and Medicine have been transformed into disciplines of applied data science over the past years. Not only size and inherentcomplexity of the data but also requirements on data privacy and complexity of search and access pose a wealth of new research questions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This interactive course will explore the latest research on algorithms and data structures for population scale genomics applications and give insights into both the technical basis as well as the domain questions motivating it. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Over the duration of the semester, the course will cover three main topics. Each of the topics will consist of 70-80% lecture content and 20-30% seminar content. 1) Algorithms and data structures for text and graph compression. Motivated through applications in compressive genomics, the course will cover succinct indexing schemes for strings, trees and general graphs, compression schemes for binary matrices as well as the efficient representation of haplotypes and genomic variants. 2) Stochastic data structures and algorithms for approximate representation of strings and graphs as well as sets in general. This includes winnowing schemes and minimizers, sketching techniques, (minimal perfect) hashing and approximate membership query data structures. 3) Data structures supporting encryption and data privacy. As an extension to data structures discussed in the earlier topics, this will include secure indexing using homomorphic encryption as well as design for secure storage and distribution of data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0834-00L | Information Systems for Engineers | W | 4 credits | 2V + 1U | G. Fourny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides the basics of relational databases from the perspective of the user. We will discover why tables are so incredibly powerful to express relations, learn the SQL query language, and how to make the most of it. The course also covers support for data cubes (analytics). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This lesson is complementary with Big Data for Engineers as they cover different time periods of database history and practices -- you can take them in any order, even though it might be more enjoyable to take this lecture first. After visiting this course, you will be capable to: 1. Explain, in the big picture, how a relational database works and what it can do in your own words. 2. Explain the relational data model (tables, rows, attributes, primary keys, foreign keys), formally and informally, including the relational algebra operators (select, project, rename, all kinds of joins, division, cartesian product, union, intersection, etc). 3. Perform non-trivial reading SQL queries on existing relational databases, as well as insert new data, update and delete existing data. 4. Design new schemas to store data in accordance to the real world's constraints, such as relationship cardinality 5. Explain what bad design is and why it matters. 6. Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of what is called "normal forms". 7. Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster. 8. Access an existing relational database from a host language such as Java, using bridges such as JDBC. 9. Explain what data independence is all about and didn't age a bit since the 1970s. 10. Explain, in the big picture, how a relational database is physically implemented. 11. Know and deal with the natural syntax for relational data, CSV. 12. Explain the data cube model including slicing and dicing. 13. Store data cubes in a relational database. 14. Map cube queries to SQL. 15. Slice and dice cubes in a UI. And of course, you will think that tables are the most wonderful object in the world. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Using a relational database ================= 1. Introduction 2. The relational model 3. Data definition with SQL 4. The relational algebra 5. Queries with SQL Taking a relational database to the next level ================= 6. Database design theory 7. Databases and host languages 8. Databases and host languages 9. Indices and optimization 10. Database architecture and storage Analytics on top of a relational database ================= 12. Data cubes Outlook ================= 13. Outlook | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - Lecture material (slides). - Book: "Database Systems: The Complete Book", H. Garcia-Molina, J.D. Ullman, J. Widom (It is not required to buy the book, as the library has it) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | For non-CS/DS students only, BSc and MSc Elementary knowledge of set theory and logics Knowledge as well as basic experience with a programming language such as Pascal, C, C++, Java, Haskell, Python | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Biology At least 10 ECTS need to be acquired in this category. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 529-0733-01L | Enzymes | W | 6 credits | 3G | D. Hilvert | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Principles of enzymatic catalysis, enzyme kinetics, mechanisms of enzyme-catalyzed reactions (group transfer reactions, carbon-carbon bond formation, eliminations, isomerisations and rearrangements), cofactor chemistry, enzymes in organic synthesis and the biosynthesis of natural products, catalytic antibodies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Overview of enzymes, enzyme-catalyzed reactions and metabolic processes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Principles of enzymatic catalysis, enzyme kinetics, mechanisms of enzyme catalyzed reactions (group transfer reactions, carbon-carbon bond formation, eliminations, isomerisations and rearrangements), cofactor chemistry, enzymes in organic synthesis and the biosynthesis of natural products, catalytic antibodies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A script will not be handed out. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | General: T. Bugg, An Introduction to Enzyme and Coenzyme Chemistry, Blackwell Science Ltd., Oxford, 1997. In addition, citations from the original literature relevant to the individual lectures will be assigned weekly. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 551-0309-00L | Concepts in Modern Genetics Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module BIO348 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | W | 6 credits | 4V | Y. Barral, D. Bopp, A. Hajnal, O. Voinnet | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Concepts of modern genetics and genomics, including principles of classical genetics; yeast genetics; gene mapping; forward and reverse genetics; structure and function of eukaryotic chromosomes; molecular mechanisms and regulation of transcription, replication, DNA-repair and recombination; analysis of developmental processes; epigenetics and RNA interference. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course focuses on the concepts of classical and modern genetics and genomics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The topics include principles of classical genetics; yeast genetics; gene mapping; forward and reverse genetics; structure and function of eukaryotic chromosomes; molecular mechanisms and regulation of transcription, replication, DNA-repair and recombination; analysis of developmental processes; epigenetics and RNA interference. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Scripts and additional material will be provided during the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 551-0313-00L | Microbiology (Part I) | W | 3 credits | 2V | W.‑D. Hardt, L. Eberl, J. Piel, M. Pilhofer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Advanced lecture class providing a broad overview on bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This concept class will be based on common concepts and introduce to the enormous diversity among bacteria and archaea. It will cover the current research on bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Advanced class covering the state of the research in bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Updated handouts will be provided during the class. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Current literature references will be provided during the lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | English The lecture "Grundlagen der Biologie II: Mikrobiologie" is the basis for this advanced lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 551-0317-00L | Immunology I | W | 3 credits | 2V | M. Kopf, A. Oxenius | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction into structural and functional aspects of the immune system. Basic knowledge of the mechanisms and the regulation of an immune response. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Introduction into structural and functional aspects of the immune system. Basic knowledge of the mechanisms and the regulation of an immune response. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Introduction and historical background - Innate and adaptive immunity, Cells and organs of the immune system - B cells and antibodies - Generation of diversity - Antigen presentation and Major Histoincompatibility (MHC) antigens - Thymus and T cell selection - Autoimmunity - Cytotoxic T cells and NK cells - Th1 and Th2 cells, regulatory T cells - Allergies - Hypersensitivities - Vaccines, immune-therapeutic interventions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Electronic access to the documentation will be provided. The link can be found at "Lernmaterialien" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - Kuby, Immunology, 9th edition, Freemen + Co., New York, 2020 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | For D-BIOL students Immunology I (WS) and Immunology II (SS) will be examined as one learning entity in a "Sessionsprüfung". All other students write separate exams for Immunology I and Immunology II. All exams (combined exam Immunology I and II, individual exams) are offered in each exam session. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 636-0105-00L | Introduction to Biological Computers | W | 4 credits | 3G | Y. Benenson | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Biological computers are man-made biological networks that interrogate and control cells and organisms in which they operate. Their key features, inspired by computer science, are programmability, modularity, and versatility. The course will show how to rationally design, implement and test biological computers using molecular engineering, DNA nanothechnology and synthetic biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course has the following objectives: * Familiarize students with parallels between theories in computer science and engineering and information-processing in live cells and organisms * Introduce basic theories of computation * Introduce approaches to creating novel biological computing systems in non-living environment and in living cells including bacteria, yeast and mammalian/human cells. The covered approaches will include - Nucleic acids engineering - DNA and RNA nanotechnology - Synthetic biology and gene circuit engineering - High-throughput genome engineering and gene circuit assembly * Equip the students with computer-aided design (CAD) tools for biocomputing circuit engineering. A number of tutorials will introduce MATLAB SimBiology toolbox for circuit design and simulations * Foster creativity, research and communication skills through semester-long "Design challenge" assignment in the broad field of biological computing and biological circuit engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Note: the exact subjects can change, the details below should only serve for general orientation Lecture 1. Introduction: what is molecular computation (part I)? * What is computing in general? * What is computing in the biological context (examples from development, chemotaxis and gene regulation) * The difference between natural computing and engineered biocomputing systems Lecture 2: What is molecular computation (part II) + State machines 1st hour * Detailed definition of an engineered biocomputing system * Basics of characterization * Design challenge presentation 2nd hour * Theories of computation: state machines (finite automata and Turing machines) Lecture 3: Additional models of computation * Logic circuits * Analog circuits * RAM machines Basic approaches to computer science notions relevant to molecular computation. (i) State machines; (ii) Boolean networks; (iii) analog computing; (iv) distributed computing. Design Challenge presentation. Lecture 4. Classical DNA computing * Adleman experiment * Maximal clique problem * SAT problem Lecture 5: Molecular State machines through self-assembly * Tiling implementation of state machine * DNA-based tiling system * DNA/RNA origami as a spin-off of self-assembling state machines Lecture 6: Molecular State machines that use DNA-encoded tapes * Early theoretical work * Tape extension system * DNA and enzyme-based finite automata for diagnostic applications Lecture 7: Introduction to cell-based logic and analog circuits * Computing with (bio)chemical reaction networks * Tuning computation with ultrasensitivity and cooperativity * Specific examples Lecture 8: Transcriptional circuits I * Introducing transcription-based circuits * General features and considerations * Guidelines for large circuit construction Lecture 9: Transcriptional circuits II * Large-scale distributed logic circuits in bacteria * Toward large-scale circuits in mammalian cells Lecture 10: RNA circuits I * General principles of RNA-centered circuit design * Riboswitches and sRNA regulation in bacteria * Riboswitches in yeast and mammalian cells * General approach to RNAi-based computing Lecture 11: RNA circuits II * RNAi logic circuits * RNAi-based cell type classifiers * Hybrid transcriptional/posttranscriptional approaches Lecture 12: In vitro DNA-based logic circuits * DNAzyme circuits playing tic-tac-toe against human opponents * DNA brain Lecture 13: Advanced topics * Engineered cellular memory * Counting and sequential logic * The role of evolution * Fail-safe design principles Lecture 14: Design challenge presentation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes will be available online | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | As a way of general introduction, the following two review papers could be useful: Benenson, Y. RNA-based computation in live cells. Current Opinion in Biotechnology 2009, 20:471:478 Benenson, Y. Biocomputers: from test tubes to live cells. Molecular Biosystems 2009, 5:675:685 Benenson, Y. Biomolecular computing systems: principles, progress and potential (Review). Nature Reviews Genetics 13, 445-468 (2012). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge of molecular biology is assumed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-0510-00L | Proteomics and Drug Discovery Research | W | 2 credits | 2V | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 636-0511-00L | Developmental Neuroscience | W | 2 credits | 2V | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 636-0515-00L | Molecular Medicine I | W | 2 credits | 2V | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 262-6170-00L | Molecular Mechanisms of Development | W | 2 credits | 2V | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 262-6180-00L | Molecular Control of Vertebrate Development and Organogenesis Does not take place this semester. | W | 2 credits | 2V | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 262-5130-00L | Evolutionary Medicine: Ancient Pathogens and Pathologies (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: BIO440 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | W | 6 credits | 5G | University lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 262-6101-00L | Antibiotic Drug Targets and Resistance Does not take place this semester. | W | 1 credit | 1V | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 262-6102-00L | Functional Organization of the Cell Nucleus Does not take place this semester. | W | 2 credits | 2V | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 262-6103-00L | Cellular Signalling Does not take place this semester. | W | 2 credits | 2V | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 262-6105-00L | Frontiers in RNA Biology | W | 2 credits | 2V | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 636-0109-00L | Stem Cells: Biology and Therapeutic Manipulation Does not take place this semester. | W | 4 credits | 3G | T. Schroeder | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Stem cells are central in tissue regeneration and repair, and hold great potential for therapy. We will discuss the role of stem cells in health and disease, and possibilities to manipulate their behavior for therapeutic application. Basic molecular and cell biology, engineering and novel technologies relevant for stem cell research and therapy will be discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understanding of current knowledge, and lack thereof, in stem cell biology, regenerative medicine and required technologies. Theoretical preparation for practical laboratory experimentation with stem cells. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | We will use different diseases to discuss how to potentially model, diagnose or heal them by stem cell based therapies. This will be used as a guiding framework to discuss relevant concepts and technologies in cell and molecular biology, engineering, imaging, bioinformatics, tissue engineering, that are required to manipulate stem cells for therapeutic application. Topics will include: - Embryonic and adult stem cells and their niches - Induced stem cells by directed reprogramming - Relevant basic cell biology and developmental biology - Relevant molecular biology - Cell culture systems - Cell fates and their molecular control by transcription factors and signalling pathways - Cell reprogramming - Disease modelling - Tissue engineering - Bioimaging, Bioinformatics - Single cell technologies | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-0108-00L | Biological Engineering and Biotechnology | W | 4 credits | 3V | M. Fussenegger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Biological Engineering and Biotechnology will cover the latest biotechnological advances as well as their industrial implementation to engineer mammalian cells for use in human therapy. This lecture will provide forefront insights into key scientific aspects and the main points in industrial decision-making to bring a therapeutic from target to market. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Biological Engineering and Biotechnology will cover the latest biotechnological advances as well as their industrial implementation to engineer mammalian cells for use in human therapy. This lecture will provide forefront insights into key scientific aspects and the main points in industrial decision-making to bring a therapeutic from target to market. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 1. Insight Into The Mammalian Cell Cycle. Cycling, The Balance Between Proliferation and Cancer - Implications For Biopharmaceutical Manufacturing. 2. The Licence To Kill. Apoptosis Regulatory Networks - Engineering of Survival Pathways To Increase Robustness of Production Cell Lines. 3. Everything Under Control I. Regulated Transgene Expression in Mammalian Cells - Facts and Future. 4. Secretion Engineering. The Traffic Jam getting out of the Cell. 5. From Target To Market. An Antibody's Journey From Cell Culture to The Clinics. 6. Biology and Malign Applications. Do Life Sciences Enable the Development of Biological Weapons? 7. Functional Food. Enjoy your Meal! 8. Industrial Genomics. Getting a Systems View on Nutrition and Health - An Industrial Perspective. 9. IP Management - Food Technology. Protecting Your Knowledge For Business. 10. Biopharmaceutical Manufacturing I. Introduction to Process Development. 11. Biopharmaceutical Manufacturing II. Up- stream Development. 12. Biopharmaceutical Manufacturing III. Downstream Development. 13. Biopharmaceutical Manufacturing IV. Pharma Development. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handout during the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-5120-00L | Principles of Evolution: Theory (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: BIO351 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | W | 6 credits | 3V | University lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | "Nothing in Biology Makes Sense Except in the Light of Evolution". Evolutionary theory and methods are essential in all branches of modern biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Subject specific skills: By the end of the course, students will be able to: o describe basic evolutionary theory and its applications o discuss ongoing debates in evolutionary biology o critically assess the presentation of evolutionary research in the popular media Key skills: By the end of the course, students will be able to: o approach biological questions from an evolutionary perspective | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course will provide a broad overview of current evolutionary thought, including the mechanisms of evolutionary change, adaptation and the history of life and will involve practical field and lab work as well as lecture material. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 551-0307-00L | Molecular and Structural Biology I: Protein Structure and Function D-BIOL students are obliged to take part I and part II (next semester) as a two-semester course | W | 3 credits | 2V | R. Glockshuber, K. Locher, E. Weber-Ban | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Biophysics of protein folding, membrane proteins and biophysics of membranes, enzymatic catalysis, catalytic RNA and RNAi, current topics in protein biophysics and structural biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understanding of structure-function relationships in proteins and in protein folding, detailed understanding of biophysics and physical methods as well as modern methods for protein purification and microanalytics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Scripts on the individual topics can be found under http://www.mol.biol.ethz.ch/teaching. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Basics: - Creighton, T.E., Proteins, Freeman, (1993) - Fersht, A., Enzyme, Structure and Mechanism in Protein Science (1999), Freeman. - Berg, Tymoczko, Stryer: Biochemistry (5th edition), Freeman (2001). Current topics: References will be given during the lectures. . | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-6107-00L | Applied Mathematics and Informatics in Drug Discovery | W | 2 credits | 2G | external organisers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Lab Rotations Students starting before Autumn Semester 2021: 18 ECTS in total (262-01*). At least two lab rotations need to be completed in two different research groups (supervisors). Either choose Lab Rotation Short 1 (6 ECTS), Lab Rotation Short 2 (6 ECTS) and Lab Rotation Short 3 (6 ECTS) Or choose Lab Rotation Long 1 (9 ECTS) and Lab Rotation Long 2 (9 ECTS) Or choose Lab Rotation Short 1 (6 ECTS) and Industry Internship (12 ECTS) Or choose Lab Rotation Short 1 (6 ECTS) and Lab Rotation Long 3 (12 ECTS) Students starting in Autumn Semester 2021 or later: 18 ECTS in total (262-03*). At least one lab rotation in different group/ supervisor than master’s thesis. Either choose Lab Rotation Short 1 and Lab Rotation Short 2 (each 6 weeks, 9 ECTS) Or choose lab Rotation Short 1 and Industry Internship Short (each 6 weeks, 9 ECTS) Or choose Lab Rotation Long (12 weeks, 18 ECTS) Or choose Industry Internship Long (12 weeks, 18 ECTS) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0100-00L | Lab Rotation Short 1 | W | 6 credits | 13A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Flexible short research project of 4 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0101-00L | Lab Rotation Short 2 | W | 6 credits | 13A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Flexible short research project of 4 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0102-00L | Lab Rotation Short 3 | W | 6 credits | 13A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Flexible short research project of 4 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0103-00L | Lab Rotation Long 1 | W | 9 credits | 19A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Flexible short research project of 6 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0104-00L | Lab Rotation Long 2 | W | 9 credits | 19A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Flexible short research project of 6 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0105-00L | Industry Internship | W | 12 credits | 26A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Industry internship of at least 8 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain experience in an industrial environment and an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The students look for a placement themselves. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0106-00L | Lab Rotation Long 3 | W | 12 credits | 26A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Flexible short research project of 8 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0300-00L | Lab Rotation Short 1 | W | 9 credits | 17A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Flexible short research project of 6 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0301-00L | Lab Rotation Short 2 | W | 9 credits | 17A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Flexible short research project of 6 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0303-00L | Lab Rotation Long | W | 18 credits | 34A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Flexible research project of 12 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0302-00L | Industry Internship Short | W | 9 credits | 17A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Industry internship of at least 6 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain experience in an industrial environment and an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The students look for a placement themselves. Industry internship lasts for 6 weeks, longer duration will delay the completion of studies beyond two years. Recognition of the industry internship requires a meaningful 10-page report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0304-00L | Industry Internship Long | W | 18 credits | 34A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Industry internship of at least 12 weeks, completed with a written report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain experience in an industrial environment and an overview of different research areas by applying concepts taught in the core courses and advanced courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The students look for a placement themselves. Industry internship lasts for 12 weeks, longer duration will delay the completion of studies beyond two years. Recognition of the industry internship requires a meaningful 10-page report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| GESS Science in Perspective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| » see GESS Science in Perspective: Language Courses ETH/UZH | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| » see GESS Science in Perspective: Type A: Enhancement of Reflection Capability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| » Recommended GESS Science in Perspective (Type B) for D-INFK. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Master's Thesis | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0800-00L | Master's Thesis Only students who fulfill the following criteria are allowed to begin with their master thesis: a. successful completion of the bachelor programme; b. fulfilling of any additional requirements necessary to gain admission to the master programme. | O | 30 credits | 64D | Professors | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The Master Thesis is the result of an independent scientific research and/or constructive development project in the chosen area of specialization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The Master thesis concludes the Master programme. By writing up the Master thesis, students show their ability to independently produce a coherent and scientific piece of work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The program concludes with a Master thesis that includes a written report and an oral presentation. The topic of the thesis can be chosen according to the student's interests in the field of computational biology & bioinformatics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The duration for the master's thesis in the study regulation 2017 (per Autumn Semester 2021) is 24 working weeks (thereof, 2 weeks are reserved for compensation of public holidays, sick leave and other unplanned short term absences.) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional requirements. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0002-AAL | Data Structures and Algorithms Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 8 credits | 15R | F. Friedrich Wicker | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course is about fundamental algorithm design paradigms (such as induction, divide-and-conquer, backtracking, dynamic programming), classic algorithmic problems (such as sorting and searching), and data structures (such as lists, hashing, search trees). Moreover, an introduction to parallel programming is provided. The programming model of C++ will be discussed in some depth. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | An understanding of the design and analysis of fundamental algorithms and data structures. Knowledge regarding chances, problems and limits of parallel and concurrent programming. Deeper insight into a modern programming model by means of the programming language C++. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Fundamental algorithms and data structures are presented and analyzed. Firstly, this comprises design paradigms for the development of algorithms such as induction, divide-and-conquer, backtracking and dynamic programming and classical algorithmic problems such as searching and sorting. Secondly, data structures for different purposes are presented, such as linked lists, hash tables, balanced search trees, heaps and union-find structures. The relationship and tight coupling between algorithms and data structures is illustrated with geometric problems and graph algorithms. In the part about parallel programming, parallel architectures are discussed conceptually (multicore, vectorization, pipelining). Parallel programming concepts are presented (Amdahl's and Gustavson's laws, task/data parallelism, scheduling). Problems of concurrency are analyzed (Data races, bad interleavings, memory reordering). Process synchronisation and communication in a shared memory system is explained (mutual exclusion, semaphores, monitors, condition variables). Progress conditions are analysed (freedom from deadlock, starvation, lock- and wait-freedom). The concepts are underpinned with examples of concurrent and parallel programs and with parallel algorithms. The programming model of C++ is discussed in some depth. The RAII (Resource Allocation is Initialization) principle will be explained. Exception handling, functors and lambda expression and generic prorgamming with templates are further examples of this part. The implementation of parallel and concurrent algorithm with C++ is also part of the exercises (e.g. threads, tasks, mutexes, condition variables, promises and futures). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Cormen, Leiserson, Rivest, and Stein: Introduction to Algorithms, 3rd ed., MIT Press, 2009. ISBN 978-0-262-03384-8 (recommended text) Maurice Herlihy, Nir Shavit, The Art of Multiprocessor Programming, Elsevier, 2012. B. Stroustrup, The C++ Programming Language (4th Edition) Addison-Wesley, 2013. B. Stroustrup, The C++ Programming Language (4th Edition) Addison-Wesley, 2013. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Lecture Series 252-0835-00L Informatik I or equivalent knowledge in programming with C++. Please note that this is a self study (virtual) course, which implies that (in the autumn semester) there are no physical lectures or exercise sessions offered. If you want to attend the real course, please go to 252-0002-00L in the spring semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0856-AAL | Computer Science Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | F. Friedrich Wicker, R. Sasse | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Die Vorlesung bietet eine Einführung in das Programmieren mit einem Fokus auf systematischem algorithmischem Problemlösen. Lehrsprache ist C++. Es wird keine Programmiererfahrung vorausgesetzt. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Primäres Lernziel der Vorlesung ist die Befähigung zum Programmieren mit C++. Studenten beherrschen nach erfolgreichem Abschluss der Vorlesung die Mechanismen zum Erstellen eines Programms, sie kennen die fundamentalen Kontrollstrukturen, Datenstrukturen und verstehen, wie man ein algorithmisches Problem in ein Programm abbildet. Sie haben eine Vorstellung davon, was "hinter den Kulissen" passiert, wenn ein Programm übersetzt und ausgeführt wird. Sekundäre Lernziele der Vorlesung sind das Computer-basierte, algorithmische Denken, Verständnis der Möglichkeiten und der Grenzen der Programmierung und die Vermittlung der Denkart eines Computerwissenschaftlers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Wir behandeln fundamentale Datentypen, Ausdrücke und Anweisungen, (Grenzen der) Computerarithmetik, Kontrollanweisungen, Funktionen, Felder, zusammengesetze Strukturen und Zeiger. Im Teil zur Objektorientierung werden Klassen, Vererbung und Polymorhpie behandelt, es werden exemplarisch einfache dynamische Datentypen eingeführt. Die Konzepte der Vorlesung werden jeweils durch Algorithmen und Anwendungen motiviert und illustriert. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Ein Skript in englischer Sprache wird semesterbegleitend herausgegeben. Das Skript und die Folien werden auf der Vorlesungshomepage zum Herunterladen bereitgestellt. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Bjarne Stroustrup: Einführung in die Programmierung mit C++, Pearson Studium, 2010 Stephen Prata: C++ Primer Plus, Sixth Edition, Addison Wesley, 2012 Andrew Koenig and Barbara E. Moo: Accelerated C++, Addison-Wesley, 2000. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 406-0603-AAL | Stochastics (Probability and Statistics) Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | M. Kalisch | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to basic methods and fundamental concepts of statistics and probability theory for non-mathematicians. The concepts are presented on the basis of some descriptive examples. Learning the statistical program R for applying the acquired concepts will be a central theme. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of this course is to build a solid fundament in probability and statistics. The student should understand some fundamental concepts and be able to apply these concepts to applications in the real world. Furthermore, the student should have a basic knowledge of the statistical programming language "R". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | From "Statistics for research" (online) Ch 1: The Role of Statistics Ch 2: Populations, Samples, and Probability Distributions Ch 3: Binomial Distributions Ch 6: Sampling Distribution of Averages Ch 7: Normal Distributions Ch 8: Student's t Distribution Ch 9: Distributions of Two Variables From "Introductory Statistics with R (online)" Ch 1: Basics Ch 2: The R Environment Ch 3: Probability and distributions Ch 4: Descriptive statistics and tables Ch 5: One- and two-sample tests Ch 6: Regression and correlation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - "Statistics for research" by S. Dowdy et. al. (3rd edition); Print ISBN: 9780471267355; Online ISBN: 9780471477433; DOI: 10.1002/0471477435 From within the ETH, this book is freely available online under: http://onlinelibrary.wiley.com/book/10.1002/0471477435 - "Introductory Statistics with R" by Peter Dalgaard; ISBN 978-0-387-79053-4; DOI: 10.1007/978-0-387-79054-1 From within the ETH, this book is freely available online under: http://www.springerlink.com/content/m17578/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 262-0945-AAL | Cell and Molecular Biology for Engineers I and II Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 6 credits | 13R | B. Treutlein | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Morgan, Raff, Roberts, and Walter. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-1005-AAL | Bio V: Bioinformatics Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 5 credits | 7R | N. Beerenwinkel | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Pevsner J, Bioinformatics and Functional Genomics, 3rd edition, 2015, chapters 1–7 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||