# Search result: Catalogue data in Autumn Semester 2015

Statistics Master The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible. | ||||||

Specialization Areas and Electives | ||||||

Statistical and Mathematical Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|---|

401-3601-00L | Probability Theory | W | 10 credits | 4V + 1U | A.‑S. Sznitman | |

Abstract | Basics of probability theory and the theory of stochastic processes in discrete time | |||||

Objective | This course presents the basics of probability theory and the theory of stochastic processes in discrete time. The following topics are planned: Basics in measure theory, random series, law of large numbers, weak convergence, characteristic functions, central limit theorem, conditional expectation, martingales, convergence theorems for martingales, Galton Watson chain, transition probability, Theorem of Ionescu Tulcea, Markov chains. | |||||

Content | This course presents the basics of probability theory and the theory of stochastic processes in discrete time. The following topics are planned: Basics in measure theory, random series, law of large numbers, weak convergence, characteristic functions, central limit theorem, conditional expectation, martingales, convergence theorems for martingales, Galton Watson chain, transition probability, Theorem of Ionescu Tulcea, Markov chains. | |||||

Lecture notes | available, will be sold in the course | |||||

Literature | R. Durrett, Probability: Theory and examples, Duxbury Press 1996 H. Bauer, Probability Theory, de Gruyter 1996 J. Jacod and P. Protter, Probability essentials, Springer 2004 A. Klenke, Wahrscheinlichkeitstheorie, Springer 2006 D. Williams, Probability with martingales, Cambridge University Press 1991 | |||||

401-3627-00L | High-Dimensional StatisticsDoes not take place this semester. | W | 4 credits | 2V | P. L. Bühlmann | |

Abstract | "High-Dimensional Statistics" deals with modern methods and theory for statistical inference when the number of unknown parameters is of much larger order than sample size. Statistical estimation and algorithms for complex models and aspects of multiple testing will be discussed. | |||||

Objective | Knowledge of methods and basic theory for high-dimensional statistical inference | |||||

Content | Lasso and Group Lasso for high-dimensional linear and generalized linear models; Additive models and many smooth univariate functions; Non-convex loss functions and l1-regularization; Stability selection, multiple testing and construction of p-values; Undirected graphical modeling | |||||

Literature | Peter Bühlmann and Sara van de Geer (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Verlag. ISBN 978-3-642-20191-2. | |||||

Prerequisites / Notice | Knowledge of basic concepts in probability theory, and intermediate knowledge of statistics (e.g. a course in linear models or computational statistics). | |||||

401-3611-00L | Advanced Topics in Computational Statistics | W | 4 credits | 2V | M. H. Maathuis, M. Mächler | |

Abstract | This lecture covers selected advanced topics in computational statistics, including various classification methods, the EM algorithm, clustering, handling missing data, and graphical modelling. | |||||

Objective | Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes. | |||||

Content | The course is roughly divided in three parts: (1) Supervised learning via (variations of) nearest neighbor methods, (2) the EM algorithm and clustering, (3) handling missing data and graphical models. | |||||

Lecture notes | Lecture notes. | |||||

Prerequisites / Notice | We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics. | |||||

401-4633-00L | Data Analytics in Organisations and Business | W | 5 credits | 2V + 1U | I. Flückiger | |

Abstract | On the end-to-end process of data analytics in organisations & business and how to transform data into insights for fact based decisions. Presentation of the process from the beginning with framing the business problem to presenting the results and making decisions by the use of data analytics. For each topic case studies from the financial service, healthcare and retail sectors will be presented. | |||||

Objective | The goal of this course is to give the students the understanding of the data analytics process in the business world, with special focus on the skills and techniques used besides the technical skills. The student will become familiar with the "business language", current problems and thinking in organisations and business and tools used. | |||||

Content | Framing the Business Problem Framing the Analytics Problem Data Methodology Model Building Deployment Model Lifecycle Soft Skills for the Statistical/Mathematical Professional | |||||

Lecture notes | Lecture Notes will be available. | |||||

Prerequisites / Notice | Prerequisites: Basic statistics and probability theory and regression | |||||

401-6217-00L | Using R for Data Analysis and Graphics (Part II) | W | 1 credit | 1G | A. J. Papritz, C. B. Schwierz | |

Abstract | The course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions. Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known. | |||||

Objective | The students will be able to use the software R efficiently for data analysis. | |||||

Content | The course provides the second part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R. Part II of the course builds on part I and covers the following additional topics: - Elements of the R language: control structures (if, else, loops), lists, overview of R objects, attributes of R objects; - More on R functions; - Applying functions to elements of vectors, matrices and lists; - Object oriented programming with R: classes and methods; - Tayloring R: options - Extending basic R: packages The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org | |||||

Lecture notes | An Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf | |||||

Prerequisites / Notice | Basic knowledge of R equivalent to "Using R .. (part 1)" ( = 401-6215-00L ) is a prerequisite for this course. The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145 Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||

401-0627-00L | Smoothing and Nonparametric Regression with Examples | W | 4 credits | 2G | S. Beran-Ghosh | |

Abstract | Starting with an overview of selected results from parametric inference, kernel smoothing (including local polynomials) will be introduced along with some asymptotic theory, optimal bandwidth selection, data driven algorithms and some special topics. Examples from environmental research will be used for motivation, but the methods will also be applicable elsewhere. | |||||

Objective | The students will learn about methods of kernel smoothing and application of concepts to data. The aim will be to build sufficient interest in the topic and intuition as well as the ability to implement the methods to various different datasets. | |||||

Content | Rough Outline: - Parametric estimation methods: selection of important results o Maximum likelihood o Least squares: regression & diagnostics - Nonparametric curve estimation o Density estimation, Kernel regression, Local polynomials, Bandwidth selection o Selection of special topics (as time permits, we will cover as many topics as possible) such as change points, modes & monotonicity, robustness, partial linear models, roughness penalty, local likelihoods, etc. - Applications: potential areas of applications will be discussed such as, change assessment, trend and surface estimation, probability and quantile curve estimation, and others. | |||||

Lecture notes | Brief summaries or outlines of some of the lecture material will be posted at http://www.wsl.ch/info/mitarbeitende/ghosh/index_EN (click on "ETH Course" in the left panel). NOTE: The posted notes will tend to be just sketches whereas only the in-class lessons will contain complete information. LOG IN: In order to have access to the posted notes, you will need the course user id & the password. These will be given out on the first day of the lectures. | |||||

Literature | References: - Statistical Inference, by S.D. Silvey, Chapman & Hall. - Regression Analysis: Theory, Methods and Applications, by A. Sen and M. Srivastava, Springer. - Density Estimation, by B.W. Silverman, Chapman and Hall. - Kernel Smoothing, by M.P. Wand and M.C. Jones, Chapman and Hall. - Local polynomial modelling and its applications, by J. Fan and I. Gijbels, Chapman & Hall. - Nonparametric Simple Regression, by J. Fox, Sage Publications. - Applied Smoothing Techniques for Data Analysis: the Kernel Approach With S-Plus Illustrations, by A.W. Bowman, A. Azzalini, Oxford University Press. Additional references will be given out in the lectures. | |||||

Prerequisites / Notice | Prerequisites: A background in Linear Algebra, Calculus, Probability & Statistical Inference including Estimation and Testing. | |||||

401-6201-00L | Resampling Methods Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to kanzlei@rektorat.ethz.ch. The Registrar's Office will then register you for the course. | W | 2 credits | 2G | L. Meier | |

Abstract | This course covers several generally useful statistical methods: Nonparametric tests, randomization tests, jackknife and bootstrap, as well as asymptotic approximations and robustness properties of estimators. | |||||

Objective | For the classical parametric models there are optimal statistical estimators and test statistics, and their distributions can often be determined exactly. The methods covered in this course allow for finding statisticsl procedures for more general models and to derive exact or approximate distributions of complicated estimators and test statistics. They thus make it possible to use specific models for any applications under consideration and to derive corresponding statistical procedures. | |||||

Content | Nonparametric tests, randomization tests, jackknife and bootstrap, asymptotic approximations and robustness properties of estimators. | |||||

Lecture notes | http://stat.ethz.ch/~meier/teaching/resampling/ | |||||

Literature | Only for parts of the course author = {A. C. Davison and D. V. Hinkley}, title = {Bootstrap methods and their application}, publisher = {Cambridge University Press}, year = 1997, note = {includes 1 disk}, series = {Cambridge Series in Statistical and Probabilistic Mathematics} | |||||

Prerequisites / Notice | This course is part of the programme for the certificate and diploma in Advanced Studies in Applied Statistics. It is given every second year in the winter semester break. | |||||

401-6221-00L | Nonparametric Regression Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to kanzlei@rektorat.ethz.ch. The Registrar's Office will then register you for the course. | W | 1 credit | 1G | ||

Abstract | This course focusses on nonparametric estimation of probability densities and regression functions. These recent methods allow modelling without restrictive assumptions such as 'linear function'. These smoothing methods require a weight function and a smoothing parameter. Focus is on one dimension, higher dimensions and samples of curves are treated briefly. Exercises at the computer. | |||||

Objective | Knowledge on estimation of probability densities and regression functions via various statistical methods. Understanding of the choice of weight function and of the smoothing parameter, also done automatically. Practical application on data sets at the computer. | |||||

401-6233-00L | Spatial Statistics Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to kanzlei@rektorat.ethz.ch. The Registrar's Office will then register you for the course. | W | 1 credit | 1G | ||

Abstract | In many research fields, spatially referenced data are collected. When analysing such data the focus is either on exploring their structure (dependence on explanatory variables, autocorrelation) and/or on spatial prediction. The course provides an introduction to geostatistical methods that are useful for such purposes. | |||||

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

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

Lecture notes | Slides, descriptions of the problems for the data analyses and worked-out solutions to them will be provided. | |||||

Literature | P.J. Diggle & P.J. Ribeiro Jr. 2007. Model-based Geostatistics. Springer | |||||

263-5200-00L | Data Mining: Learning from Large Data Sets | W | 4 credits | 2V + 1U | A. Krause | |

Abstract | Many scientific and commercial applications require insights from massive, high-dimensional data sets. This courses introduces principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course both covers theoretical foundations and practical applications. | |||||

Objective | Many scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In this graduate-level course, we will study principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course will both cover theoretical foundations and practical applications. | |||||

Content | Topics covered: - Dealing with large data (Data centers; Map-Reduce/Hadoop; Amazon Mechanical Turk) - Fast nearest neighbor methods (Shingling, locality sensitive hashing) - Online learning (Online optimization and regret minimization, online convex programming, applications to large-scale Support Vector Machines) - Multi-armed bandits (exploration-exploitation tradeoffs, applications to online advertising and relevance feedback) - Active learning (uncertainty sampling, pool-based methods, label complexity) - Dimension reduction (random projections, nonlinear methods) - Data streams (Sketches, coresets, applications to online clustering) - Recommender systems | |||||

Prerequisites / Notice | Prerequisites: Solid basic knowledge in statistics, algorithms and programming. Background in machine learning is helpful but not required. | |||||

401-6245-00L | Data Mining Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to kanzlei@rektorat.ethz.ch. The Registrar's Office will then register you for the course. | W | 1 credit | 1G | ||

Abstract | Block course only on prediction problems, aka "supervised learning". Part 1, Classification: logistic regression, linear/quadratic discriminant analysis, Bayes classifier; additive and tree models; further flexible ("nonparametric") methods. Part 2, Flexible Prediction: additive models, MARS, Y-Transformation models (ACE,AVAS); Projection Pursuit Regression (PPR), neural nets. | |||||

Objective | ||||||

Content | "Data Mining" is a large field from which in this block course, we only treat so called prediction problems, aka "supervised learning". Part 1, Classification, recalls logistic regression and linear / quadratic discriminant analysis (LDA/QDA) and extends these (in the framework of 'Bayes classifier") to (generalized) additive (GAM) and tree models (CART), and further mentions other flexible ("nonparametric") methods. Part 2, Flexible Prediction (of continuous or "class" response/target) contains additive models, MARS, Y-Transformation models (ACE, AVAS); Projection Pursuit Regression (PPR), neural nets. | |||||

Lecture notes | The block course is based on (German language) lecture notes. | |||||

Prerequisites / Notice | The exercises are done exlusively with the (free, open source) software "R" (http://www.r-project.org). A final exam will also happen at the computers, using R (and your brains!). | |||||

401-6289-00L | Sampling Surveys Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to kanzlei@rektorat.ethz.ch. The Registrar's Office will then register you for the course. | W | 2 credits | 1G | ||

Abstract | The elements of a sample survey are explained. The most important classical sample designs (simple random sampling and stratified random sampling) with their estimation procedures and the use of auxiliary information including the Horvitz-Thompson estimator are introduced. Data preparation, non-response and its treatment, variance estimation and analysis of survey data is discussed. | |||||

Objective | Knowledge of the Elements and the process of a sample survey. Understanding of the paradigm of random samples. Knowledge of simple random samplinig and stratified random sampling and capability to apply the corresponding methods. Knowledge of further methods of sampling and estimation as well as data preparation and analysis. | |||||

401-6273-00L | Bayes Methods Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to kanzlei@rektorat.ethz.ch. The Registrar's Office will then register you for the course. | W | 2 credits | 2G | ||

Abstract | Bayes statistics is attractive, because it allows to make decisions under uncertainty where a classical frequentist statistical approach fails. The course provides an introduction into bayesian methods. It is moderately mathematically technical, but demands a flexibility of mind, which should not underestimated. | |||||

Objective | ||||||

Content | conditional probability; bayes inference (conjugate distributions, HPD-areas; linear and empirical bayes); determination of the a-posteriori distribution through simulation (MCMC with R2Winbugs); introduction to multilevel/hierarchical models. | |||||

Literature | Gelman A., Carlin J.B., Stern H.S. and D.B. Rubin, Bayesian Data Analysis, Chapman and Hall, 2nd Edition, 2004. Kruschke, J.K., Doing Bayesian Data Analysis, Elsevier2011. | |||||

Prerequisites / Notice | Prerequisite:Basic knowledge of statistics; Knowledge of R. | |||||

401-3913-01L | Mathematical Foundations for Finance | W | 4 credits | 3V + 2U | E. W. Farkas, M. Schweizer | |

Abstract | First introduction to main modelling ideas and mathematical tools from mathematical finance | |||||

Objective | This course gives a first introduction to the main modelling ideas and mathematical tools from mathematical finance. It aims at a double audience: mathematicians who want to learn the modelling ideas and concepts for finance, and non-mathematicians who need an introduction to the main tools from stochastics used in mathematical finance. The main emphasis will be on ideas, but important results will be given with (sometimes partial) proofs. | |||||

Content | Topics to be covered include - financial market models in finite discrete time - absence of arbitrage and martingale measures - valuation and hedging in complete markets - basics about Brownian motion - stochastic integration - stochastic calculus: Itô's formula, Girsanov transformation, Itô's representation theorem - Black-Scholes formula | |||||

Lecture notes | Lecture notes will be sold at the beginning of the course. | |||||

Literature | Lecture notes will be sold at the beginning of the course. Additional (background) references are given there. | |||||

Prerequisites / Notice | Prerequisites: Results and facts from probability theory as in the book "Probability Essentials" by J. Jacod and P. Protter will be used freely. Especially participants without a direct mathematics background are strongly advised to familiarise themselves with those tools before (or very quickly during) the course. (A possible alternative to the above English textbook are the (German) lecture notes for the standard course "Wahrscheinlichkeitstheorie".) For those who are not sure about their background, we suggest to look at the exercises in Chapters 8, 9, 22-25, 28 of the Jacod/Protter book. If these pose problems, you will have a hard time during the course. So be prepared. | |||||

401-3901-00L | Mathematical Optimization | W | 11 credits | 4V + 2U | R. Weismantel | |

Abstract | Mathematical treatment of diverse optimization techniques. | |||||

Objective | Advanced optimization theory and algorithms. | |||||

Content | 1. Linear optimization: The geometry of linear programming, the simplex method for solving linear programming problems, Farkas' Lemma and infeasibility certificates, duality theory of linear programming. 2. Nonlinear optimization: Lagrange relaxation techniques, Newton method and gradient schemes for convex optimization. 3. Integer optimization: Ties between linear and integer optimization, total unimodularity, complexity theory, cutting plane theory. 4. Combinatorial optimization: Network flow problems, structural results and algorithms for matroids, matchings and, more generally, independence systems. | |||||

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. UZH Module Code: STA426 Mind the enrolment deadlines at UZH: http://www.uzh.ch/studies/application/mobilitaet_en.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. | |||||

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 | |||||

401-8625-00L | Statistical Methods in Clinical Research (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: STA404 Mind the enrolment deadlines at UZH: http://www.uzh.ch/studies/application/mobilitaet_en.html | W | 5 credits | 3G | University lecturers | |

Abstract | Discussion of the different statistical methods that are used in clinical research. | |||||

Objective | ||||||

Content | Discussion of the different statistical methods that are used in clinical research. Among other subjects the following will be introduced: sample size calculation, randomization and blinding, analysis of clinical trials (parallel groups design, analysis of covariance, crossover design, equivalence studies), intention-to-treat analysis, multiple testing, group sequential methods, adaptive designs, diagnostic studies, and agreement studies. | |||||

Literature | - Matthews, J. N. S. (2006). Introduction to Randomized Controlled Clinical Trials. Chapman & Hall/CRC Texts in Statistical Science. - Cook, T. D. and DeMets, L. D. (2008). Introduction to Statistical Methods for Clinical Trials. Chapman & Hall/CRC Texts in Statistical Science. - Pepe, M. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. - Schumacher, M. and Schulgen, G. (2008). Methodik klinischer Studien. Springer, Berlin. | |||||

Prerequisites / Notice | Basic knowlegde of the programming language R, sufficient knowledge in calculus, linear algebra, probability, statistics | |||||

252-0535-00L | Machine Learning | W | 6 credits | 3V + 2U | J. M. Buhmann | |

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. | |||||

Objective | Students will be familiarized with the most important 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. A machine learning project 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: - Bayesian theory of optimal decisions - Maximum likelihood and Bayesian parameter inference - Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM) - Ensemble methods: Bagging and Boosting - Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off - Non parametric density estimation: Parzen windows, nearest nieghbour - Dimension reduction: principal component analysis (PCA) and beyond | |||||

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 | Solid basic knowledge in analysis, statistics and numerical methods for CSE. Experience in programming for solving the project tasks. | |||||

252-0523-00L | Computational Biology | W | 6 credits | 3V + 2U | G. H. Gonnet | |

Abstract | Study of computational techniques, algorithms and data structures used to solve problems in computational biology. Topics: basic biology, string alignment, phylogeny (distance, character, parsimony), molecular evolution, multiple sequence alignment, probabilistic and statistical models, Markov models, microarrays, dynamic programming, maximum likelihood and specialized DNA and protein analysis. | |||||

Objective | Familiarize the students with the basic concepts of molecular biology and the models and algorithms used to understand, classify and predict behaviour of living organism. This course is at the most basic level, where the main issues, mostly of molecular sequences, are studied. | |||||

Content | This course lies in the intersection between Computer Science and Molecular Biology. The main purpose is to study computational techniques, algorithms and data structures which are usually applied to solve problems in Molecular Biology and Biochemistry. The following topics are likely to be covered: Introduction, mathematical models of evolution, protein and DNA sequence alignment and its meaning, phylogenetic tree construction, multiple sequence alignments, secondary structure prediction, molecular dynamics, threading, role of bioinformatics in drug design, etc. From the computer science point of view we concentrate our attention in practical solutions for the above problems. Biological knowledge is an asset but not a prerequisite. |

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