Suchergebnis: Katalogdaten im Herbstsemester 2016
Computational Biology and Bioinformatics Master More informations at: Link | ||||||
Kernfächer | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|
262-5120-00L | Principles of Evolution: Theory (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: BIO351 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 6 KP | 3V | Uni-Dozierende | |
Kurzbeschreibung | "Nothing in Biology Makes Sense Except in the Light of Evolution". Evolutionary theory and methods are essential in all branches of modern biology. | |||||
Lernziel | 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 | |||||
Inhalt | 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. | |||||
401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: STA426 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 5 KP | 3G | H. Rehrauer, M. Robinson | |
Kurzbeschreibung | 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. | |||||
Lernziel | -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 | |||||
Inhalt | 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 | |||||
Skript | Lecture notes, published manuscripts | |||||
Voraussetzungen / Besonderes | 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 | |||||
551-0307-00L | Molecular and Structural Biology I: Protein Structure and Function D-BIOL BSc students are obliged to take part I and part II (next semester) as a two-semester course | W | 3 KP | 2V | R. Glockshuber, K. Locher, E. Weber-Ban | |
Kurzbeschreibung | Biophysik der Proteinfaltung, Membranproteine und Biophysik von Membranen, enzymatischen Katalyse, katalytische RNA und RNAi, aktuelle Themen in Proteinbiophysik und Strukturbiologie. | |||||
Lernziel | Verständnis von Struktur/Funktionsbeziehungen in Proteinen, Proteinfaltung, Vertiefung der Kenntnisse in Biophysik, in physikalischen Messmethoden und modernen Methoden der Proteinreinigung und Protein-Mikroanalytik. | |||||
Skript | Skripte zu einzelnen Themen der Vorlesung sind unter Link abgelegt. | |||||
Literatur | Grundlagen: - 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). Aktuelle Themen: Literatur wird jeweils in der Vorlesung angegeben | |||||
636-0007-00L | Computational Systems Biology | W | 6 KP | 3V + 2U | J. Stelling | |
Kurzbeschreibung | 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). | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Skript | Link | |||||
Literatur | U. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006. Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2006. | |||||
636-0009-00L | Evolutionary Dynamics | W | 5 KP | 2V + 1U | N. Beerenwinkel | |
Kurzbeschreibung | 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. | |||||
Lernziel | The goal of this course is to understand and to appreciate mathematical models and computational methods that provide insight into the evolutionary process. | |||||
Inhalt | 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, birth-death processes, evolutionary stability, evolutionary graph theory, somatic evolution of cancer, stochastic tunneling, cell differentiation, hematopoietic tumor stem cells, genetic progression of cancer and the speed of adaptation, diffusion theory, fitness landscapes, neutral networks, branching processes, evolutionary escape, and epistasis. | |||||
Skript | No. | |||||
Literatur | - 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. | |||||
Voraussetzungen / Besonderes | Prerequisites: Basic mathematics (linear algebra, calculus, probability) | |||||
636-0017-00L | Computational Biology | W | 4 KP | 3G | T. Stadler, C. Magnus | |
Kurzbeschreibung | 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. | |||||
Lernziel | Attendees will learn which information is contained in genetic sequencing data and how to extract information from them 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 | |||||
Inhalt | 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 to directly analyze this alignment (such as BLAST algorithm, GWAS approaches). 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. We finally introduce the field of phylodynamics. The aim of that field is to understand and quantify the 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. | |||||
Skript | Slides of the lecture will be available online. Link | |||||
Literatur | 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 | |||||
Voraussetzungen / Besonderes | Basic knowledge in linear algebra, analysis, and statistics will be helpful. Some programming experience will be useful for the exercises, but is not required. Programming skills will not be tested in the examination. | |||||
636-0706-00L | Spatio-Temporal Modelling in Biology | W | 5 KP | 3G | D. Iber | |
Kurzbeschreibung | This course focuses on modeling spatio-temporal problems in biology, in particular on the cell and tissue level. A wide range of mathematical techniques will be presented as part of the course, including concepts from non-linear dynamics (ODE and PDE models), stochastic techniques (SDE, Master equations, Monte Carlo simulations), and thermodynamic descriptions. | |||||
Lernziel | The aim of the course is to introduce students to state-of-the-art mathematical modelling of spatio-temporal problems in biology. Students will learn how to chose from a wide range of modelling techniques and how to apply these to further our understanding of biological mechanisms. The course aims at equipping students with the tools and concepts to conduct successful research in this area; both classical as well as recent research work will be discussed. | |||||
Inhalt | 1. Introduction to Modelling in Biology 2. Morphogen Gradients 3. Turing Pattern 4. Travelling Waves & Wave Pinning 5. Application Example 1: Dorso-ventral axis formation 6. Chemotaxis, Cell Adhesion & Migration 7. Introduction to Numerical Methods 8. Simulations on Growing Domains 9. Image-Based Modelling 10. Branching Processes 11. Cell-based Simulation Frameworks 12. Application Example 2: Limb Development 13. Summary | |||||
Skript | All lecture material will be made available online Link | |||||
Literatur | 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 | |||||
Voraussetzungen / Besonderes | The course builds on introductory courses in Computational Biology. The course assumes no background in biology but a good foundation regarding mathematical and computational techniques. | |||||
Vertiefungsfächer und Methoden der Informatik | ||||||
Vertiefungsfächer | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
252-0025-00L | Diskrete Mathematik | W | 7 KP | 4V + 2U | U. Maurer | |
Kurzbeschreibung | Inhalt: Mathematisches Denken und Beweise, Abstraktion. Mengen, Relationen (z.B. Aequivalenz- und Ordnungsrelationen), Funktionen, (Un-)abzählbarkeit, Zahlentheorie, Algebra (Gruppen, Ringe, Körper, Polynome, Unteralgebren, Morphismen), Logik (Aussagen- und Prädikatenlogik, Beweiskalküle). | |||||
Lernziel | Hauptziele der Vorlesung sind (1) die Einführung der wichtigsten Grundbegriffe der diskreten Mathematik, (2) das Verständnis der Rolle von Abstraktion und von Beweisen und (3) die Diskussion einiger Anwendungen, z.B. aus der Kryptographie, Codierungstheorie und Algorithmentheorie. | |||||
Inhalt | Siehe Kurzbeschreibung. | |||||
Skript | vorhanden (englisch) | |||||
227-1033-00L | Neuromorphic Engineering I Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. | W | 6 KP | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |
Kurzbeschreibung | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||
Lernziel | Understanding of the characteristics of neuromorphic circuit elements. | |||||
Inhalt | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||
Literatur | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||
Voraussetzungen / Besonderes | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||
227-1037-00L | Introduction to Neuroinformatics | W | 6 KP | 2V + 1U | K. A. Martin, M. Cook, V. Mante, M. Pfeiffer | |
Kurzbeschreibung | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||
Lernziel | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||
Inhalt | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, the neural architectures of feedforward and recurrent networks will be discussed in the context of co-ordination, control, and integration of sensory and motor information in neural networks. | |||||
529-0004-00L | Computer Simulation in Chemistry, Biology and Physics | W | 7 KP | 4G | P. H. Hünenberger | |
Kurzbeschreibung | Molecular models, Force fields, Boundary conditions, Electrostatic interactions, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. For more information: Link | |||||
Lernziel | Introduction to computer simulation of (bio)molecular systems, development of skills to carry out and interpret computer simulations of biomolecular systems. | |||||
Inhalt | Molecular models, Force fields, Spatial boundary conditions, Calculation of Coulomb forces, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. | |||||
Skript | Available (copies of powerpoint slides distributed before each lecture) | |||||
Literatur | See: Link | |||||
Voraussetzungen / Besonderes | Since the exercises on the computer do convey and test essentially different skills as 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. For more information about the lecture: Link | |||||
529-0733-00L | Enzymes | W | 7 KP | 3G | D. Hilvert | |
Kurzbeschreibung | Vermittlung eines Überblicks über die Chemie von Enzymen, enzymkatalysierten Reaktionen, metabolischen Prozessen. | |||||
Lernziel | Vermittlung eines Überblicks über die Chemie von Enzymen, enzymkatalysierten Reaktionen, metabolischen Prozessen. | |||||
Inhalt | Prinzipien der enzymatischen Katalyse, Enzymkinetiken, Mechanismen enzymkatalysierter Reaktionen (Gruppentransferreaktion, Kohlenstoff-Kohlenstoff-Bindungsknüpfungen, Eliminierungen, Isomerisierungen und Umlagerungen), Kofaktorenchemie, Enzyme in der organischen Synthese und in der Naturstoffbiosynthese, katalytische Antikörper. | |||||
Skript | A script will not be handed out. | |||||
Literatur | 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. | |||||
535-0810-00L | Gentechnologie | W | 2 KP | 2G | D. Neri | |
Kurzbeschreibung | The course will provide a solid overview of the science and issues in gene technology and its pharmaceutical applications. | |||||
Lernziel | The aim of the lecture course is to provide a solid overview of gene technology, with a special focus on drug development. Topics: Antibody phage technology, DNA-encoded chemistry, protein modification technology, genome sequencing, transcriptomics, proteomics, functional genomics, principle of drug discovery. The course is suited for advanced undergraduate and early graduate students in pharmaceutical sciences or related fields. | |||||
Inhalt | 1. Antibody phage technology The antibody molecule V genes, CDRs, basics of antibody engineering Principles of phage display Phagemid and phage vectors Antibody libraries Phage display selection methodologies Other phage libraries (peptides, globular proteins, enzymes) Alternative screening/selection methodologies DNA-encoded chemical libraries 2. Proteins: chemical modification and detection of biomolecular interactions Homo- and hetero-dimerization of proteins Chemical modifications of proteins Antibody-drug conjugates Radioactive labeling of proteins Kinetic association and dissociation constants Affinity constant: definition and its experimental measurement 3. Genomics: Applications to Human Biology Protein cloning and expression DNA sequencing Some foundations of genetic analysis Knock-out technologies Transcriptomics Proteomics Recombinant vaccines 4: Pharmaceuticals: Focus on Discovery Ligand Discovery Half-life extension Cancer therapy Gene therapy | |||||
Skript | Skript "Gene Technology" by Prof. Dario Neri and slides of the lecture | |||||
551-0307-00L | Molecular and Structural Biology I: Protein Structure and Function D-BIOL BSc students are obliged to take part I and part II (next semester) as a two-semester course | W | 3 KP | 2V | R. Glockshuber, K. Locher, E. Weber-Ban | |
Kurzbeschreibung | Biophysik der Proteinfaltung, Membranproteine und Biophysik von Membranen, enzymatischen Katalyse, katalytische RNA und RNAi, aktuelle Themen in Proteinbiophysik und Strukturbiologie. | |||||
Lernziel | Verständnis von Struktur/Funktionsbeziehungen in Proteinen, Proteinfaltung, Vertiefung der Kenntnisse in Biophysik, in physikalischen Messmethoden und modernen Methoden der Proteinreinigung und Protein-Mikroanalytik. | |||||
Skript | Skripte zu einzelnen Themen der Vorlesung sind unter Link abgelegt. | |||||
Literatur | Grundlagen: - 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). Aktuelle Themen: Literatur wird jeweils in der Vorlesung angegeben | |||||
551-0309-00L | Concepts in Modern Genetics | W | 6 KP | 4V | Y. Barral, D. Bopp, A. Hajnal, M. Stoffel, O. Voinnet | |
Kurzbeschreibung | 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. | |||||
Lernziel | This course focuses on the concepts of classical and modern genetics and genomics. | |||||
Inhalt | 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. | |||||
Skript | Scripts and additional material will be provided during the semester. | |||||
Voraussetzungen / Besonderes | This course is a co-production of the University of Zurich and ETH Zurich, and will be taught in English. The course takes place on Monday afternoon at ETH Hoenggerberg, and on Tuesday morning at UZH Irchel. | |||||
551-0313-00L | Microbiology (Part I) | W | 3 KP | 2V | W.‑D. Hardt, L. Eberl, H.‑M. Fischer, J. Piel, M. Pilhofer | |
Kurzbeschreibung | Advanced lecture class providing a broad overview on bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||
Lernziel | 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. | |||||
Inhalt | Advanced class covering the state of the research in bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis. | |||||
Skript | Updated handouts will be provided during the class. | |||||
Literatur | Current literature references will be provided during the lectures. | |||||
Voraussetzungen / Besonderes | English The lecture "Grundlagen der Biologie II: Mikrobiologie" is the basis for this advanced lecture. | |||||
551-0317-00L | Immunology I | W | 3 KP | 2V | A. Oxenius, M. Kopf | |
Kurzbeschreibung | Einführung in strukturelle und funktionelle Eigenschaften des Immunsystems. Grundlegendes Verständnis der Mechanismen und der Regulation einer Immunantwort. | |||||
Lernziel | Einführung in strukturelle und funktionelle Eigenschaften des Immunsystems. Grundlegendes Verständnis der Mechanismen und der Regulation einer Immunantwort. | |||||
Inhalt | - Einleitung und historischer Hintergrund - Angeborene und adaptive Immunantwort, Zellen und Organe des Immunsystems - B Zellen und Antikörper - Generation von Diversität - Antigen-Präsentation und Histoinkompatibilitätsantigene (MHC) - Thymus und T Zellselektion - Autoimmunität - Zytotoxische T Zellen und NK Zellen - Th1 und Th2 Zellen, regulatorische T Zellen - Allergien - Hypersensitivititäten - Impfungen und immun-therapeutische Interventionen | |||||
Skript | Die Studenten haben elekronischen Zugriff auf die Vorlesungsunterlagen. Der Link ist unter "Lernmaterialien" zu finden. | |||||
Literatur | - Kuby, Immunology, 7th edition, Freemen + Co., New York, 2009 | |||||
Voraussetzungen / Besonderes | Immunology I (WS) und Immunology II (SS) werden in einer Sessionsprüfung im Anschluss an Immunology II als eine Lerneinheit geprüft. | |||||
401-0647-00L | Introduction to Mathematical Optimization | W | 5 KP | 2V + 1U | D. Adjiashvili | |
Kurzbeschreibung | Introduction to basic techniques and problems in mathematical optimization, and their applications to problems in engineering. | |||||
Lernziel | 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. | |||||
Inhalt | Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, network flows, knapsack problem, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering. | |||||
Literatur | Information about relevant literature will be given in the lecture. | |||||
Voraussetzungen / Besonderes | This course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics and more. | |||||
Methoden der Informatik | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
252-0057-00L | Theoretische Informatik | W | 8 KP | 4V + 2U + 1A | J. Hromkovic | |
Kurzbeschreibung | Konzepte zur Beantwortung grundlegender Fragen wie: a) Was ist völlig automatisiert machbar (algorithmisch lösbar) b) Wie kann man die Schwierigkeit von Aufgaben (Problemen) messen? c) Was ist Zufall und wie kann er nützlich sein? d) Was ist Nichtdeterminisus und welche Rolle spielt er in der Informatik? e) Wie kann man unendliche Objekte durch Automaten und Grammatiken endlich darstellen? | |||||
Lernziel | Vermittlung der grundlegenden Konzepte der Informatik in ihrer geschichtlichen Entwicklung | |||||
Inhalt | Die Veranstaltung ist eine Einführung in die Theoretische Informatik, die die grundlegenden Konzepte und Methoden der Informatik in ihrem geschichtlichen Zusammenhang vorstellt. Wir präsentieren Informatik als eine interdisziplinäre Wissenschaft, die auf einer Seite die Grenzen zwischen Möglichem und Unmöglichem und die quantitativen Gesetze der Informationsverarbeitung erforscht und auf der anderen Seite Systeme entwirft, analysiert, verifiziert und implementiert. Die Hauptthemen der Vorlesung sind: - Alphabete, Wörter, Sprachen, Messung der Informationsgehalte von Wörtern, Darstellung von algorithmischen Aufgaben - endliche Automaten, reguläre und kontextfreie Grammatiken - Turingmaschinen und Berechenbarkeit - Komplexitätstheorie und NP-Vollständigkeit - Algorithmenentwurf für schwere Probleme | |||||
Skript | Die Vorlesung ist detailliert durch das Lehrbuch "Theoretische Informatik" bedeckt. | |||||
Literatur | Basisliteratur: 1. J. Hromkovic: Theoretische Informatik. 5. Auflage, Springer Vieweg 2014. 2. J. Hromkovic: Theoretical Computer Science. Springer 2004. Weiterführende Literatur: 3. M. Sipser: Introduction to the Theory of Computation, PWS Publ. Comp.1997 4. J.E. Hopcroft, R. Motwani, J.D. Ullman: Einführung in die Automatentheorie, Formale Sprachen und Komplexitätstheorie. Pearson 2002. 5. I. Wegener: Theoretische Informatik. Teubner Weitere Übungen und Beispiele: 6. A. Asteroth, Ch. Baier: Theoretische Informatik | |||||
Voraussetzungen / Besonderes | Während des Semesters werden zwei freiwillige Probeklausuren gestellt. | |||||
252-0535-00L | Machine Learning | W | 8 KP | 3V + 2U + 2A | J. M. Buhmann | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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 | |||||
Skript | No lecture notes, but slides will be made available on the course webpage. | |||||
Literatur | 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. | |||||
Voraussetzungen / Besonderes | 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 at least have followed one previous course offered by the Machine Learning Institute (e.g., CIL or LIS) or an equivalent course offered by another institution. |
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