Search result: Catalogue data in Spring Semester 2019
Computational Science and Engineering Bachelor | ||||||
For All Programme Regulations | ||||||
Fields of Specialization | ||||||
Astrophysics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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402-0394-00L | Theoretical Astrophysics and Cosmology UZH students are not allowed to register this course unit at ETH. They must book the corresponding module directly at UZH. | W | 10 credits | 4V + 2U | L. M. Mayer, J. Yoo | |
Abstract | This is the second of a two course series which starts with "General Relativity" and continues in the spring with "Theoretical Astrophysics and Cosmology", where the focus will be on applying general relativity to cosmology as well as developing the modern theory of structure formation in a cold dark matter Universe. | |||||
Objective | Learning the fundamentals of modern physical cosmology. This entails understanding the physical principles behind the description of the homogeneous Universe on large scales in the first part of the course, and moving on to the inhomogeneous Universe model where perturbation theory is used to study the development of structure through gravitational instability in the second part of the course. Modern notions of dark matter and dark energy will also be introduced and discussed. | |||||
Content | The course will cover the following topics: - Homogeneous cosmology - Thermal history of the universe, recombination, baryogenesis and nucleosynthesis - Dark matter and Dark Energy - Inflation - Perturbation theory: Relativistic and Newtonian - Model of structure formation and initial conditions from Inflation - Cosmic microwave background anisotropies - Spherical collapse and galaxy formation - Large scale structure and cosmological probes | |||||
Literature | Suggested textbooks: H.Mo, F. Van den Bosch, S. White: Galaxy Formation and Evolution S. Carroll: Space-Time and Geometry: An Introduction to General Relativity S. Dodelson: Modern Cosmology Secondary textbooks: S. Weinberg: Gravitation and Cosmology V. Mukhanov: Physical Foundations of Cosmology E. W. Kolb and M. S. Turner: The Early Universe N. Straumann: General relativity with applications to astrophysics A. Liddle and D. Lyth: Cosmological Inflation and Large Scale Structure | |||||
Prerequisites / Notice | Knowledge of General Relativity is recommended. | |||||
Physics of the Atmosphere | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-1216-00L | Numerical Modelling of Weather and Climate | W | 4 credits | 3G | C. Schär, N. Ban | |
Abstract | The course provides an introduction to weather and climate models. It discusses how these models are built addressing both the dynamical core and the physical parameterizations, and it provides an overview of how these models are used in numerical weather prediction and climate research. As a tutorial, students conduct a term project and build a simple atmospheric model using the language PYTHON. | |||||
Objective | At the end of this course, students understand how weather and climate models are formulated from the governing physical principles, and how they are used for climate and weather prediction purposes. | |||||
Content | The course provides an introduction into the following themes: numerical methods (finite differences and spectral methods); adiabatic formulation of atmospheric models (vertical coordinates, hydrostatic approximation); parameterization of physical processes (e.g. clouds, convection, boundary layer, radiation); atmospheric data assimilation and weather prediction; predictability (chaos-theory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction. Hands-on experience with simple models will be acquired in the tutorials. | |||||
Lecture notes | Slides and lecture notes will be made available at Link | |||||
Literature | List of literature will be provided. | |||||
Prerequisites / Notice | Prerequisites: to follow this course, you need some basic background in atmospheric science, numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) as well as experience in programming. Previous experience with PYTHON is useful but not required. | |||||
Chemistry | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
529-0474-00L | Quantum Chemistry | W | 6 credits | 3G | M. Reiher, T. Weymuth | |
Abstract | Introduction into the basic concepts of electronic structure theory and into numerical methods of quantum chemistry. Exercise classes are designed to deepen the theory; practical case studies using quantum chemical software to provide a 'hands-on' expertise in applying these methods. | |||||
Objective | Nowadays, chemical research can be carried out in silico, an intellectual achievement for which Pople and Kohn have been awarded the Nobel prize of the year 1998. This lecture shows how that has been accomplished. It works out the many-particle theory of many-electron systems (atoms and molecules) and discusses its implementation into computer programs. A complete picture of quantum chemistry shall be provided that will allow students to carry out such calculations on molecules (for accompanying experimental work in the wet lab or as a basis for further study of the theory). | |||||
Content | Basic concepts of many-particle quantum mechanics. Derivation of the many-electron theory for atoms and molecules; starting with the harmonic approximation for the nuclear problem and with Hartree-Fock theory for the electronic problem to Moeller-Plesset perturbation theory and configuration interaction and to coupled cluster and multi-configurational approaches. Density functional theory. Case studies using quantum mechanical software. | |||||
Lecture notes | Hand outs in German will be provided for each lecture (they are supplemented by (computer) examples that continuously illustrate how the theory works). Please navigate to the lecture material starting here: Link | |||||
Literature | Textbooks on Quantum Chemistry: F.L. Pilar, Elementary Quantum Chemistry, Dover Publications I.N. Levine, Quantum Chemistry, Prentice Hall Hartree-Fock in basis set representation: A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, McGraw-Hill Textbooks on Computational Chemistry: F. Jensen, Introduction to Computational Chemistry, John Wiley & Sons C.J. Cramer, Essentials of Computational Chemistry, John Wiley & Sons | |||||
Prerequisites / Notice | Basic knowledge in quantum mechanics (e.g. through course physical chemistry III - quantum mechanics) required | |||||
227-0161-00L | Molecular and Materials Modelling | W | 4 credits | 2V + 2U | D. Passerone, C. Pignedoli | |
Abstract | "Molecular and Materials Modelling" introduces the basic techniques to interpret experiments with contemporary atomistic simulation. These techniques include force fields or density functional theory (DFT) based molecular dynamics and Monte Carlo. Structural and electronic properties, thermodynamic and kinetic quantities, and various spectroscopies will be simulated for nanoscale systems. | |||||
Objective | The ability to select a suitable atomistic approach to model a nanoscale system, and to employ a simulation package to compute quantities providing a theoretically sound explanation of a given experiment. This includes knowledge of empirical force fields and insight in electronic structure theory, in particular density functional theory (DFT). Understanding the advantages of Monte Carlo and molecular dynamics (MD), and how these simulation methods can be used to compute various static and dynamic material properties. Basic understanding on how to simulate different spectroscopies (IR, STM, X-ray, UV/VIS). Performing a basic computational experiment: interpreting the experimental input, choosing theory level and model approximations, performing the calculations, collecting and representing the results, discussing the comparison to the experiment. | |||||
Lecture notes | A script will be made available. | |||||
Literature | D. Frenkel and B. Smit, Understanding Molecular Simulations, Academic Press, 2002. M. P. Allen and D.J. Tildesley, Computer Simulations of Liquids, Oxford University Press 1990. Andrew R. Leach, Molecular Modelling, principles and applications, Pearson, 2001 | |||||
Fluid Dynamics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
151-0208-00L | Computational Methods for Flow, Heat and Mass Transfer Problems | W | 4 credits | 2V + 2U | D. W. Meyer-Massetti | |
Abstract | Numerical methods for the solution of flow, heat & mass transfer problems are presented and illustrated by analytical & computer exercises. | |||||
Objective | Knowledge of and practical experience with discretization and solution methods for computational fluid dynamics and heat and mass transfer problems | |||||
Content | - Introduction with application examples, steps to a numerical solution - Classification of PDEs, application examples - Finite differences - Finite volumes - Method of weighted residuals, spectral methods, finite elements - Stability analysis, consistency, convergence - Numerical solution methods, linear solvers The learning materials are illustrated with practical examples. | |||||
Lecture notes | Slides to be completed during the lecture will be handed out. | |||||
Literature | References are provided during the lecture. Notes in close agreement with the lecture material are available (in German). | |||||
Prerequisites / Notice | Basic knowledge in fluid dynamics, thermodynamics and programming (Computational Methods for Engineering Applications) | |||||
Systems and Control | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0216-00L | Control Systems II | W | 6 credits | 4G | R. Smith | |
Abstract | Introduction to basic and advanced concepts of modern feedback control. | |||||
Objective | Introduction to basic and advanced concepts of modern feedback control. | |||||
Content | This course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues. | |||||
Lecture notes | The slides of the lecture are available to download. | |||||
Literature | Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005. | |||||
Prerequisites / Notice | Prerequisites: Control Systems or equivalent | |||||
227-0046-10L | Signals and Systems II | W | 4 credits | 2V + 2U | F. Dörfler | |
Abstract | Continuous and discrete time linear system theory, state space methods, frequency domain methods, controllability, observability, stability. | |||||
Objective | Introduction to basic concepts of system theory. | |||||
Content | Modeling and classification of dynamical systems. Modeling of linear, time invariant systems by state equations. Solution of state equations by time domain and Laplace methods. Stability, controllability and observability analysis. Frequency domain description, Bode and Nyquist plots. Sampled data and discrete time systems. Advanced topics: Nonlinear systems, chaos, discrete event systems, hybrid systems. | |||||
Lecture notes | Copy of transparencies | |||||
Literature | Recommended: K.J. Astrom and R. Murray, "Feedback Systems: An Introduction for Scientists and Engineers", Princeton University Press 2009 Link | |||||
Prerequisites / Notice | THE LECTURE WILL BE GIVEN IN ENGLISH. | |||||
Robotics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
151-0854-00L | Autonomous Mobile Robots | W | 5 credits | 4G | R. Siegwart, M. Chli, J. Nieto | |
Abstract | The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples. | |||||
Objective | The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. | |||||
Lecture notes | This lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept. | |||||
Literature | This lecture is based on the Textbook: Introduction to Autonomous Mobile Robots Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356 | |||||
151-0566-00L | Recursive Estimation | W | 4 credits | 2V + 1U | R. D'Andrea | |
Abstract | Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way. | |||||
Objective | Learn the basic recursive estimation methods and their underlying principles. | |||||
Content | Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle. | |||||
Lecture notes | Lecture notes available on course website: Link | |||||
Prerequisites / Notice | Requirements: Introductory probability theory and matrix-vector algebra. | |||||
252-0220-00L | Introduction to Machine Learning Previously called Learning and Intelligent Systems. | W | 8 credits | 4V + 2U + 1A | A. Krause | |
Abstract | The course introduces the foundations of learning and making predictions based on data. | |||||
Objective | The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. | |||||
Content | - Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM) | |||||
Literature | Textbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press | |||||
Prerequisites / Notice | Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Probabilistic Graphical Models - Seminar "Advanced Topics in Machine Learning" | |||||
252-0579-00L | 3D Vision | W | 4 credits | 3G | M. Pollefeys, V. Larsson | |
Abstract | The course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization. | |||||
Objective | After attending this course, students will: 1. understand the core concepts for recovering 3D shape of objects and scenes from images and video. 2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications. 3. have a good overview over the current state-of-the art in 3D vision. 4. be able to critically analyze and asses current research in this area. | |||||
Content | The goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications. | |||||
Physics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
402-0812-00L | Computational Statistical Physics | W | 8 credits | 2V + 2U | L. Böttcher | |
Abstract | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | |||||
Objective | The lecture will give a deeper insight into computer simulation methods in statistical physics. Thus, it is an ideal continuation of the lecture "Introduction to Computational Physics" of the autumn semester. In the first part students learn to apply the following methods: Classical Monte Carlo-simulations, finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Moreover, students learn about the application of statistical physics methods to Boltzmann machines and how to simulate non-equilibrium systems. In the second part, students apply molecular dynamics simulation methods. This part includes long range interactions, Ewald summation and discrete elements. | |||||
Content | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | |||||
Lecture notes | Lecture notes and slides are available online and will be distributed if desired. | |||||
Literature | Literature recommendations and references are included in the lecture notes. | |||||
Prerequisites / Notice | Some basic knowledge about statistical physics, classical mechanics and computational methods is recommended. | |||||
402-0810-00L | Computational Quantum Physics | W | 8 credits | 2V + 2U | S. Huber | |
Abstract | This course provides an introduction to simulation methods for quantum systems, starting with the one-body problem and finishing with quantum field theory, with special emphasis on quantum many-body systems. Both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, quantum Monte Carlo) are covered. | |||||
Objective | The goal is to become familiar with computer simulation techniques for quantum physics, through lectures and practical programming exercises. | |||||
227-0161-00L | Molecular and Materials Modelling | W | 4 credits | 2V + 2U | D. Passerone, C. Pignedoli | |
Abstract | "Molecular and Materials Modelling" introduces the basic techniques to interpret experiments with contemporary atomistic simulation. These techniques include force fields or density functional theory (DFT) based molecular dynamics and Monte Carlo. Structural and electronic properties, thermodynamic and kinetic quantities, and various spectroscopies will be simulated for nanoscale systems. | |||||
Objective | The ability to select a suitable atomistic approach to model a nanoscale system, and to employ a simulation package to compute quantities providing a theoretically sound explanation of a given experiment. This includes knowledge of empirical force fields and insight in electronic structure theory, in particular density functional theory (DFT). Understanding the advantages of Monte Carlo and molecular dynamics (MD), and how these simulation methods can be used to compute various static and dynamic material properties. Basic understanding on how to simulate different spectroscopies (IR, STM, X-ray, UV/VIS). Performing a basic computational experiment: interpreting the experimental input, choosing theory level and model approximations, performing the calculations, collecting and representing the results, discussing the comparison to the experiment. | |||||
Lecture notes | A script will be made available. | |||||
Literature | D. Frenkel and B. Smit, Understanding Molecular Simulations, Academic Press, 2002. M. P. Allen and D.J. Tildesley, Computer Simulations of Liquids, Oxford University Press 1990. Andrew R. Leach, Molecular Modelling, principles and applications, Pearson, 2001 | |||||
Computational Finance Offered in the autumn semester. | ||||||
Electromagnetics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0707-00L | Optimization Methods for Engineers | W | 3 credits | 2G | P. Leuchtmann | |
Abstract | First half of the semester: Introduction to the main methods of numerical optimization with focus on stochastic methods such as genetic algorithms, evolutionary strategies, etc. Second half of the semester: Each participant implements a selected optimizer and applies it on a problem of practical interest. | |||||
Objective | Numerical optimization is of increasing importance for the development of devices and for the design of numerical methods. The students shall learn to select, improve, and combine appropriate procedures for efficiently solving practical problems. | |||||
Content | Typical optimization problems and their difficulties are outlined. Well-known deterministic search strategies, combinatorial minimization, and evolutionary algorithms are presented and compared. In engineering, optimization problems are often very complex. Therefore, new techniques based on the generalization and combination of known methods are discussed. To illustrate the procedure, various problems of practical interest are presented and solved with different optimization codes. | |||||
Lecture notes | PDF of a short script (32 pages) plus the view graphs are provided | |||||
Prerequisites / Notice | Lecture in the first half of the semester, exercises in form of small projects in the second half, presentation of the results in the last week of the semester. | |||||
Geophysics Recommended combinations: Subject 1 + Subject 2 Subject 1 + Subject 3 Subject 2 + Subject 3 Subject 3 + Subject 4 Subject 5 + Subject 6 Subject 5 + Subject 4 | ||||||
Geophysics: Subject 1 offered in the autumn semester | ||||||
Geophysics: Subject 2 offered in the autumn semester | ||||||
Geophysics: Subject 3 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4008-00L | Dynamics of the Mantle and Lithosphere | W | 3 credits | 2G | A. Rozel | |
Abstract | The goal of this course is to obtain a detailed understanding of the physical properties, structure, and dynamical behavior of the mantle-lithosphere system, focusing mainly on Earth but also discussing how these processes occur differently in other terrestrial planets. | |||||
Objective | The goal of this course is to obtain a detailed understanding of the physical properties, structure, and dynamical behavior of the mantle-lithosphere system, focusing mainly on Earth but also discussing how these processes occur differently in other terrestrial planets. | |||||
Geophysics: Subject 4 recognition requires the successful completion of both course units | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4094-00L | Numerical Modelling for Applied Geophysics I | W | 3 credits | 2G | J. Robertsson | |
Abstract | This course provides an introduction to numerical modelling techniques as they are employed in many projects in Applied Geophysics. The focus is rather on the basic principles and applications than on rigorous mathematical proofs. Prerequisites for this course include (i) basic knowledge of vector analysis and Fourier transform techniques and (ii) knowledge of Matlab (required for the exercises). | |||||
Objective | After this course the students should have a good overview of the numerical modelling techniques that are commonly applied in Applied Geophysics. They should be familiar with the basic principles of the methods. Furthermore, they should know advantages and disadvantages as well as the limitations of the individual approaches. | |||||
Content | During the first part of the course, the following topics are covered: - General issues about finite precision of numerical modeling - Potential field modeling - Layered Earth modeling using transform methods - Finite differences - Finite elements - Other numerical methods Most of these modules are accompanied by exercises Small projects will be assigned to the students. They either include a programming exercise or applications of existing modelling codes. | |||||
Lecture notes | Presentation slides and some background material will be provided. | |||||
Prerequisites / Notice | This course is offered as a half-semester course during the first part of the semester | |||||
651-4096-00L | Inverse Theory for Geophysics I: Basics | W | 3 credits | 2V | A. Fichtner | |
Abstract | This course provides an introduction to inversion theory. The focus is rather on the basic principles and applications than on rigorous mathematical proofs. Prerequisites for this course include (i) basic knowledge of analysis and linear algebra and (ii) knowledge of Matlab (required for the exercises). | |||||
Objective | After this course the students should have a good grasp of geophysical inversion problems. In particular, they should be familiar with linear and non-linear inversion techniques. Most importantly, they should be aware of potential pitfalls and limitations of the methods. | |||||
Content | During this course, the following topics are covered: - Introduction to geophysical inversion - Matrix inversion techniques - Linear inversion problems - Non-linear inversion problems - Probabilistic inversion approaches - Global optimizers Most of these modules are accompanied by exercises | |||||
Lecture notes | Presentation slides and some background material will be provided. | |||||
Prerequisites / Notice | This course is offered as a half-semester course during the first part of the semester | |||||
Geophysics: Subject 5 offered in the autumn semester | ||||||
Geophysics: Subject 6 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4006-00L | Seismology of the Spherical Earth | W | 3 credits | 3G | M. van Driel, S. C. Stähler | |
Abstract | Brief review of continuum mechanics and the seismic wave equation; P and S waves; reciprocity and representation theorems; eikonal equation and ray tracing; Huygens and Fresnel; surface-waves; normal-modes; seismic interferometry and noise; numerical solutions. | |||||
Objective | After taking this course, students will have the background knowledge necessary to start an original research project in quantitative seismology. | |||||
Literature | Shearer, P., Introduction to Seismology, Cambridge University Press, 1999. Aki, K. and P. G. Richards, Quantitative Seismology, second edition, University Science Books, Sausalito, 2002. Nolet, G., A Breviary of Seismic Tomography, Cambridge University Press, 2008. | |||||
Prerequisites / Notice | This is a quantitative lecture with an emphasis on mathematical description of wave propagation phenomena on the global scale, hence basic knowledge in vector calculus, linear algebra and analysis as well as seismology (e.g. from the 'wave propagation' lecture) are essential to follow this course. | |||||
Biology | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
636-0702-00L | Statistical Models in Computational Biology | W | 6 credits | 2V + 1U + 2A | N. Beerenwinkel | |
Abstract | The course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods. | |||||
Objective | The goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets. | |||||
Content | Graphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises. | |||||
Lecture notes | no | |||||
Literature | - Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252 - Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007. - Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004 |
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