Suchergebnis: Katalogdaten im Herbstsemester 2019

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
Neural Systems and Computation Master
Wahlfächer
401-0151-00L	Lineare Algebra	W	5 KP	3V + 2U	V. C. Gradinaru
Kurzbeschreibung	Inhalt: Lineare Gleichungssysteme - der Algorithmus von Gauss, Matrizen - LR-Zerlegung, Determinanten, Vektorräume, Ausgleichsrechnung - QR-Zerlegung, Lineare Abbildungen, Eigenwertproblem, Normalformen -Singulärwertzerlegung; numerische Aspekte; Einführung in MATLAB.
Lernziel	Einführung in die Lineare Algebra für Ingenieure unter Berücksichtigung numerischer Aspekte
Skript	K. Nipp / D. Stoffer, Lineare Algebra, vdf Hochschulverlag, 5. Auflage 2002
Literatur	K. Nipp / D. Stoffer, Lineare Algebra, vdf Hochschulverlag, 5. Auflage 2002
401-0603-00L	Stochastik	W	4 KP	2V + 1U	C. Czichowsky
Kurzbeschreibung	Die Vorlesung deckt folgende Themenbereiche ab: Zufallsvariablen, Wahrscheinlichkeit und Wahrscheinlichkeitsverteilungen, gemeinsame und bedingte Wahrscheinlichkeiten und Verteilungen, das Gesetz der Grossen Zahlen, der zentrale Grenzwertsatz, deskriptive Statistik, schliessende Statistik, Statistik bei normalverteilten Daten, Punktschätzungen, und Vergleich zweier Stichproben.
Lernziel	Kenntnis der Grundlagen der Wahrscheinlichkeitstheorie und Statistik.
Inhalt	Einführung in die Wahrscheinlichkeitstheorie, einige Grundbegriffe der mathematischen Statistik und Methoden der angewandten Statistik.
Skript	Vorlesungsskript
Literatur	Vorlesungsskript
402-0811-00L	Programming Techniques for Scientific Simulations I	W	5 KP	4G	R. Käppeli
Kurzbeschreibung	This lecture provides an overview of programming techniques for scientific simulations. The focus is on basic and advanced C++ programming techniques and scientific software libraries. Based on an overview over the hardware components of PCs and supercomputer, optimization methods for scientific simulation codes are explained.
Lernziel
402-0809-00L	Introduction to Computational Physics	W	8 KP	2V + 2U	L. Böttcher
Kurzbeschreibung	Diese Vorlesung bietet eine Einführung in Computersimulationsmethoden für physikalische Probleme und deren Implementierung auf PCs und Supercomputern. Die betrachteten Themen beinhalten: klassische Bewegungsgleichungen, partielle Differentialgleichungen (Wellengleichung, Diffussionsgleichung, Maxwell-Gleichungen), Monte-Carlo Simulationen, Perkolation, Phasenübergänge und komplexe Netzwerke.
Lernziel	Studenten lernen die folgenden Methoden anzuwenden: Prinzipien zur Erstellung von Zufallszahlen, Berechnung von kritischen Exponenten am Beispiel von Perkolation, Numerische Lösung von Problemen aus der klassichen Mechanik und Elektrodynamik, Kanonische Monte-Carlo Simulationen zur numerischen Betrachtung von magnetischen Systemen. Studenten lernen auch die Verwendung verschiedener Programmiersprachen und Bibliotheken zur Lösung physikalischer Probleme kennen. Zusätzlich lernen Studenten verschiedene numerische Verfahren zu unterscheiden und gezielt zur Lösung eines gegebenen physikalischen Problems einzusetzen.
Inhalt	Einführung in die rechnergestützte Simulation physikalischer Probleme. Anhand einfacher Modelle aus der klassischen Mechanik, Elektrodynamik und statistischen Mechanik sowie interdisziplinären Anwendungen werden die wichtigsten objektorientierten Programmiermethoden für numerische Simulationen (überwiegend in C++) erläutert. Daneben wird ein Überblick über vorhandene Softwarebibliotheken für numerische Simulationen geboten.
Skript	Skript und Folien sind online verfügbar und werden bei Bedarf verteilt.
Literatur	Literaturempfehlungen und Referenzen sind im Skript enthalten.
Voraussetzungen / Besonderes	Vorlesung und Übung in Englisch, Prüfung wahlweise auf Deutsch oder Englisch
327-0703-00L	Electron Microscopy in Material Science	W	4 KP	2V + 2U	K. Kunze, R. Erni, S. Gerstl, F. Gramm, A. Käch, F. Krumeich, M. Willinger
Kurzbeschreibung	A comprehensive understanding of the interaction of electrons with condensed matter and details on the instrumentation and methods designed to use these probes in the structural and chemical analysis of various materials.
Lernziel	A comprehensive understanding of the interaction of electrons with condensed matter and details on the instrumentation and methods designed to use these probes in the structural and chemical analysis of various materials.
Inhalt	This course provides a general introduction into electron microscopy of organic and inorganic materials. In the first part, the basics of transmission- and scanning electron microscopy are presented. The second part includes the most important aspects of specimen preparation, imaging and image processing. In the third part, recent applications in materials science, solid state physics, structural biology, structural geology and structural chemistry will be reported.
Skript	will be distributed in English
Literatur	Goodhew, Humphreys, Beanland: Electron Microscopy and Analysis, 3rd. Ed., CRC Press, 2000 Thomas, Gemming: Analytical Transmission Electron Microscopy - An Introduction for Operators, Springer, Berlin, 2014 Thomas, Gemming: Analytische Transmissionselektronenmikroskopie: Eine Einführung für den Praktiker, Springer, Berlin, 2013 Williams, Carter: Transmission Electron Microscopy, Plenum Press, 1996 Reimer, Kohl: Transmission Electron Microscopy, 5th Ed., Berlin, 2008 Erni: Aberration-corrected imaging in transmission electron microscopy, Imperial College Press (2010, and 2nd ed. 2015)
402-0341-00L	Medical Physics I	W	6 KP	2V + 1U	P. Manser
Kurzbeschreibung	Introduction to the fundamentals of medical radiation physics. Functional chain due to radiation exposure from the primary physical effect to the radiobiological and medically manifest secondary effects. Dosimetric concepts of radiation protection in medicine. Mode of action of radiation sources used in medicine and its illustration by means of Monte Carlo simulations.
Lernziel	Understanding the functional chain from primary physical effects of ionizing radiation to clinical radiation effects. Dealing with dose as a quantitative measure of medical exposure. Getting familiar with methods to generate ionizing radiation in medicine and learn how they are applied for medical purposes. Eventually, the lecture aims to show the students that medical physics is a fascinating and evolving discipline where physics can directly be used for the benefits of patients and the society.
Inhalt	The lecture is covering the basic principles of ionzing radiation and its physical and biological effects. The physical interactions of photons as well as of charged particles will be reviewed and their consequences for medical applications will be discussed. The concept of Monte Carlo simulation will be introduced in the excercises and will help the student to understand the characteristics of ionizing radiation in simple and complex situations. Fundamentals in dosimetry will be provided in order to understand the physical and biological effects of ionizing radiation. Deterministic as well as stochastic effects will be discussed and fundamental knowledge about radiation protection will be provided. In the second part of the lecture series, we will cover the generation of ionizing radiation. By this means, the x-ray tube, the clinical linear accelarator, and different radioactive sources in radiology, radiotherapy and nuclear medicine will be addressed. Applications in radiolgoy, nuclear medicine and radiotherapy will be described with a special focus on the physics underlying these applications.
Skript	A script will be provided.
227-1047-00L	Consciousness: From Philosophy to Neuroscience (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI410 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html	W	3 KP	2V	D. Kiper
Kurzbeschreibung	This seminar reviews the philosophical and phenomenological as well as the neurobiological aspects of consciousness. The subjective features of consciousness are explored, and modern research into its neural substrate, particularly in the visual domain, is explained. Emphasis is placed on students developing their own thinking through a discussion-centered course structure.
Lernziel	The course's goal is to give an overview of the contemporary state of consciousness research, with emphasis on the contributions brought by modern cognitive neuroscience. We aim to clarify concepts, explain their philosophical and scientific backgrounds, and to present experimental protocols that shed light on on a variety of consciousness related issues.
Inhalt	The course includes discussions of scientific as well as philosophical articles. We review current schools of thought, models of consciousness, and proposals for the neural correlate of consciousness (NCC).
Skript	None
Literatur	We display articles pertaining to the issues we cover in the class on the course's webpage.
Voraussetzungen / Besonderes	Since we are all experts on consciousness, we expect active participation and discussions!
402-0674-00L	Physics in Medical Research: From Atoms to Cells	W	6 KP	2V + 1U	B. K. R. Müller
Kurzbeschreibung	Scanning probe and diffraction techniques allow studying activated atomic processes during early stages of epitaxial growth. For quantitative description, rate equation analysis, mean-field nucleation and scaling theories are applied on systems ranging from simple metallic to complex organic materials. The knowledge is expanded to optical and electronic properties as well as to proteins and cells.
Lernziel	The lecture series is motivated by an overview covering the skin of the crystals, roughness analysis, contact angle measurements, protein absorption/activity and monocyte behaviour. As the first step, real structures on clean surfaces including surface reconstructions and surface relaxations, defects in crystals are presented, before the preparation of clean metallic, semiconducting, oxidic and organic surfaces are introduced. The atomic processes on surfaces are activated by the increase of the substrate temperature. They can be studied using scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The combination with molecular beam epitaxy (MBE) allows determining the sizes of the critical nuclei and the other activated processes in a hierarchical fashion. The evolution of the surface morphology is characterized by the density and size distribution of the nanostructures that could be quantified by means of the rate equation analysis, the mean-field nucleation theory, as well as the scaling theory. The surface morphology is further characterized by defects and nanostructure's shapes, which are based on the strain relieving mechanisms and kinetic growth processes. High-resolution electron diffraction is complementary to scanning probe techniques and provides exact mean values. Some phenomena are quantitatively described by the kinematic theory and perfectly understood by means of the Ewald construction. Other phenomena need to be described by the more complex dynamical theory. Electron diffraction is not only associated with elastic scattering but also inelastic excitation mechanisms that reflect the electronic structure of the surfaces studied. Low-energy electrons lead to phonon and high-energy electrons to plasmon excitations. Both effects are perfectly described by dipole and impact scattering. Thin-films of rather complex organic materials are often quantitatively characterized by photons with a broad range of wavelengths from ultra-violet to infra-red light. Asymmetries and preferential orientations of the (anisotropic) molecules are verified using the optical dichroism and second harmonic generation measurements. Recently, ellipsometry has been introduced to on-line monitor film thickness, and roughness with sub-nanometer precision. These characterisation techniques are vital for optimising the preparation of medical implants. Cell-surface interactions are related to the cell adhesion and the contractile cellular forces. Physical means have been developed to quantify these interactions. Other physical techniques are introduced in cell biology, namely to count and sort cells, to study cell proliferation and metabolism and to determine the relation between cell morphology and function. X rays are more and more often used to characterise the human tissues down to the nanometer level. The combination of highly intense beams only some micrometers in diameter with scanning enables spatially resolved measurements and the determination of tissue's anisotropies of biopsies.
227-0427-00L	Signal Analysis, Models, and Machine Learning	W	6 KP	4G	H.‑A. Loeliger
Kurzbeschreibung	Mathematical methods in signal processing and machine learning. I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity. II. Learning linear and nonlinear functions and filters: neural networks, kernel methods. III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events.
Lernziel	The course is an introduction to some basic topics in signal processing and machine learning.
Inhalt	Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis. Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events.
Skript	Lecture notes.
Voraussetzungen / Besonderes	Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory
252-0535-00L	Advanced 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 advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
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: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems
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 have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.

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