Angelika Steger: Katalogdaten im Herbstsemester 2019

Auszeichnung: Die Goldene Eule
NameFrau Prof. Dr. Angelika Steger
LehrgebietInformatik (Theoretische Informatik)
Adresse
Inst. f. Theoretische Informatik
ETH Zürich, OAT Z 27
Andreasstrasse 5
8092 Zürich
SWITZERLAND
E-Mailsteger@inf.ethz.ch
URLhttp://www.cadmo.ethz.ch/as/people/professor/asteger/index
DepartementInformatik
BeziehungOrdentliche Professorin

NummerTitelECTSUmfangDozierende
252-0209-00LAlgorithms, Probability, and Computing Information 8 KP4V + 2U + 1AA. Steger, B. Gärtner, M. Ghaffari, D. Steurer
KurzbeschreibungAdvanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).
LernzielStudying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory.
SkriptWill be handed out.
LiteraturIntroduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest;
Randomized Algorithms by R. Motwani und P. Raghavan;
Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf.
252-0417-00LRandomized Algorithms and Probabilistic Methods8 KP3V + 2U + 2AA. Steger
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.
SkriptYes.
Literatur- Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995)
- Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005)
252-0851-00LAlgorithmen und Komplexität4 KP2V + 1UJ. Lengler, A. Steger
KurzbeschreibungEinführung: RAM-Maschine, Datenstrukturen; Algorithmen: Sortieren, Medianbest., Matrixmultiplikation, kürzeste Pfade, min. spann. Bäume; Paradigmen: Divide&Conquer, dynam. Programmierung, Greedy; Datenstrukturen: Suchbäume, Wörterbücher, Priority Queues; Komplexitätstheorie: Klassen P und NP, NP-vollständig, Satz von Cook, Beispiele für Reduktionen.
LernzielNach dieser Vorlesung kennen die Studierenden einige Algorithmen und übliche Werkzeuge. Sie kennen die Grundlagen der Komplexitätstheorie und können diese verwenden um Probleme zu klassifizieren.
InhaltDie Vorlesung behandelt den Entwurf und die Analyse von Algorithmen und Datenstrukturen. Die zentralen Themengebiete sind: Sortieralgorithmen, Effiziente Datenstrukturen, Algorithmen für Graphen und Netzwerke, Paradigmen des Algorithmenentwurfs, Klassen P und NP, NP-Vollständigkeit, Approximationsalgorithmen.
SkriptJa. Wird zu Beginn des Semesters verteilt.
252-4202-00LSeminar in Theoretical Computer Science Information 2 KP2SA. Steger, B. Gärtner, M. Ghaffari, M. Hoffmann, J. Lengler, D. Steurer, B. Sudakov
KurzbeschreibungPräsentation wichtiger und aktueller Arbeiten aus der theoretischen Informatik, sowie eigener Ergebnisse von Diplomanden und Doktoranden.
LernzielDas Lernziel ist, Studierende an die aktuelle Forschung heranzuführen und sie in die Lage zu versetzen, wissenschaftliche Arbeiten zu lesen, zu verstehen, und zu präsentieren.
Voraussetzungen / BesonderesThis seminar takes place as part of the joint research seminar of several theory groups. Intended participation is for students with excellent performance only. Formal minimal requirement is passing of one of the courses Algorithms, Probability, and Computing, Randomized Algorithms and Probabilistic Methods, Geometry: Combinatorics and Algorithms, Advanced Algorithms. (If you cannot fulfill this restriction, because this is your first term at ETH, but you believe that you satisfy equivalent criteria, please send an email with a detailed description of your reasoning to the organizers of the seminar.)
263-0006-00LAlgorithms Lab Belegung eingeschränkt - Details anzeigen
Only for master students, otherwise a special permission by the student administration of D-INFK is required.
8 KP4P + 3AA. Steger
KurzbeschreibungStudents learn how to solve algorithmic problems given by a textual description (understanding problem setting, finding appropriate modeling, choosing suitable algorithms, and implementing them). Knowledge of basic algorithms and data structures is assumed; more advanced material and usage of standard libraries for combinatorial algorithms are introduced in tutorials.
LernzielThe objective of this course is to learn how to solve algorithmic problems given by a textual description. This includes appropriate problem modeling, choice of suitable (combinatorial) algorithms, and implementing them (using C/C++, STL, CGAL, and BGL).
LiteraturT. Cormen, C. Leiserson, R. Rivest: Introduction to Algorithms, MIT Press, 1990.
J. Hromkovic, Teubner: Theoretische Informatik, Springer, 2004 (English: Theoretical Computer Science, Springer 2003).
J. Kleinberg, É. Tardos: Algorithm Design, Addison Wesley, 2006.
H. R. Lewis, C. H. Papadimitriou: Elements of the Theory of Computation, Prentice Hall, 1998.
T. Ottmann, P. Widmayer: Algorithmen und Datenstrukturen, Spektrum, 2012.
R. Sedgewick: Algorithms in C++: Graph Algorithms, Addison-Wesley, 2001.