Das Herbstsemester 2020 findet in einer gemischten Form aus Online- und Präsenzunterricht statt.
Bitte lesen Sie die publizierten Informationen zu den einzelnen Lehrveranstaltungen genau.

Suchergebnis: Katalogdaten im Herbstsemester 2016

Informatik DZ Information
Detaillierte Informationen zum Ausbildungsgang auf: www.didaktischeausbildung.ethz.ch
Fachwissenschaftliche Vertiefung mit pädagogischem Fokus
272-0400-00LMentorierte Arbeit Fachwissenschaftliche Vertiefung mit pädagogischem Fokus Informatik A Information Belegung eingeschränkt - Details anzeigen W+2 KP4AJ. Hromkovic, G. Serafini
KurzbeschreibungIn der mentorierten Arbeit in FV verknüpfen die Studierenden gymnasiale und universitäre Aspekte des Fachs mit dem Ziel, ihre Lehrkompetenz im Hinblick auf curriculare Entscheidungen und auf die zukünftige Entwicklung des Unterrichts zu stärken.
Angeleitet erstellen sie Texte, welche die anvisierte Leserschaft, in der Regel gymnasiale Fachlehrpersonen, unmittelbar verstehen.
LernzielDas Ziel ist, dass die Studierenden
- sich in ein neues Thema einarbeiten, indem sie Materialien beschaffen und die Quellen studieren und so ihre Fachkompetenz gezielt erweitern können.
- selbständig einen Text über den Gegenstandentwickeln und dabei einen speziellen Fokus auf die mathematische Verständlichkeit in Bezug auf den Kenntnisstand der anvisierten Leser/Leserinnen legen können.
- Möglichkeiten berufsbezogener fachlicher Weiterbildung ausprobieren.
InhaltThematische Schwerpunkte:
Die mentorierte Arbeit in FV besteht in der Regel in einer Literaturarbeit über ein Thema, das einen Bezug zum gymnasialem Unterricht oder seiner Weiterentwicklung hat. Die Studierenden setzen darin Erkenntnisse aus den Vorlesungen in FV praktisch um.

Alle Studierenden erhalten ein individuelles Thema und erstellen dazu eine eigenständige Arbeit. Sie werden dabei von ihrer Betreuungsperson begleitet. Gegebenenfalls stellen sie ihre Arbeit oder Aspekte daraus in einem Kurzvortrag vor. Die mentorierte
Arbeit ist Teil des Portfolios der Studierenden.
LiteraturDie Literatur ist themenspezifisch. Sie muss je nach Situation selber beschafft werden oder wird zur Verfügung gestellt.
Voraussetzungen / BesonderesDie Arbeit sollte vor Beginn des Praktikums abgeschlossen werden.
263-2800-00LDesign of Parallel and High-Performance Computing Information W7 KP3V + 2U + 1AT. Hoefler, M. Püschel
KurzbeschreibungAdvanced topics in parallel / concurrent programming.
LernzielUnderstand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large concurrent software systems. Become able to distinguish parallelism in problem space and in machine space. Become familiar with important technical concepts and with concurrency folklore.
252-0341-01LInformation Retrieval Information W4 KP2V + 1UT. Hofmann
KurzbeschreibungIntroduction to information retrieval with a focus on text documents and images. Main topics comprise extraction of characteristic features from documents, index structures, retrieval models, search algorithms, benchmarking, and feedback mechanisms. Searching the web, images and XML collections demonstrate recent applications of information retrieval and their implementation.
LernzielIn depth understanding of managing, indexing, and retrieving documents with text, image and XML content. Knowledge about basic search algorithms on the web, benchmarking of search algorithms, and relevance feedback methods.
252-0535-00LMachine Learning Information W8 KP3V + 2U + 2AJ. M. Buhmann
KurzbeschreibungMachine 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.
LernzielStudents 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.
InhaltThe 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
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. 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 / BesonderesThe 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.
252-1407-00LAlgorithmic Game Theory Information W7 KP3V + 2U + 1AP. Widmayer, P. Penna
KurzbeschreibungGame theory provides a formal model to study the behavior and interaction of self-interested users and programs in large-scale distributed computer systems without central control. The course discusses algorithmic aspects of game theory.
LernzielLearning the basic concepts of game theory and mechanism design, acquiring the computational paradigm of self-interested agents, and using these concepts in the computational and algorithmic setting.
InhaltThe Internet is a typical example of a large-scale distributed computer system without central control, with users that are typically only interested in their own good. For instance, they are interested in getting high bandwidth for themselves, but don't care about others, and the same is true for computational load or download rates. Game theory provides a particularly well-suited model for the behavior and interaction of such selfish users and programs. Classic game theory dates back to the 1930s and typically does not consider algorithmic aspects at all. Only a few years back, algorithms and game theory have been considered together, in an attempt to reconcile selfish behavior of independent agents with the common good.

This course discusses algorithmic aspects of game-theoretic models, with a focus on recent algorithmic and mathematical developments. Rather than giving an overview of such developments, the course aims to study selected important topics in depth.

- Introduction to classic game-theoretic concepts.
- Existence of stable solutions (equilibria), algorithms for computing equilibria, computational complexity.
- Speed of convergence of natural game playing dynamics such as best-response dynamics or regret minimization.
- Techniques for bounding the quality-loss due to selfish behavior versus optimal outcomes under central control (a.k.a. the 'Price of Anarchy').
- Design and analysis of mechanisms that induce truthful behavior or near-optimal outcomes at equilibrium.
- Selected current research topics, such as Google's Sponsored Search Auction, the U.S. FCC Spectrum Auction, Kidney Exchange.
SkriptNo lecture notes.
Literatur"Algorithmic Game Theory", edited by N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani, Cambridge University Press, 2008;

"Game Theory and Strategy", Philip D. Straffin, The Mathematical Association of America, 5th printing, 2004

Several copies of both books are available in the Computer Science library.
Voraussetzungen / BesonderesAudience: Although this is a Computer Science course, we encourage the participation from all students who are interested in this topic.

Requirements: You should enjoy precise mathematical reasoning. You need to have passed a course on algorithms and complexity. No knowledge of game theory is required.
252-0417-00LRandomized Algorithms and Probabilistic MethodsW7 KP3V + 2U + 1AA. Steger, E. Welzl
KurzbeschreibungLas Vegas & Monte Carlo algorithms; inequalities of Markov, Chebyshev, Chernoff; negative correlation; Markov chains: convergence, rapidly mixing; generating functions; Examples include: min cut, median, balls and bins, routing in hypercubes, 3SAT, card shuffling, random walks
LernzielAfter this course students will know fundamental techniques from probabilistic combinatorics for designing randomized algorithms and will be able to apply them to solve typical problems in these areas.
InhaltRandomized Algorithms are algorithms that "flip coins" to take certain decisions. This concept extends the classical model of deterministic algorithms and has become very popular and useful within the last twenty years. In many cases, randomized algorithms are faster, simpler or just more elegant than deterministic ones. In the course, we will discuss basic principles and techniques and derive from them a number of randomized methods for problems in different areas.
Literatur- Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995)
- Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005)
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