Game theory provides a good model for the behavior and interaction of the selfish users and programs in large-scale distributed computer systems without central control. The course discusses algorithmic aspects of game theory: Introduction to game theory, Auction-like mechanisms, Cost of a central control optimum and a selfish equilibrium, Algorithms and complexity of computing equilibria.

Latest Topics in Algorithms and Comlexity will be discussed.

Latest Topics in Algorithms and Comlexity will be discussed.

query processing, optimization, stream-based systems, distributed and parallel databases, non-standard databases

Query processing, optimization, stream-based systems, distributed and parallel databases, non-standard databases.

Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Network Flows, Minimum Cut, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).

Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Network Flows, Minimum Cut, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).

Introduction to the theory of approximation algorithms and complexity classes, examples include knapsack, bin packing, metric TSP, TSP in planar graphs, Euclidean TSP, Steiner trees; PCP-theorem, APX-reductions; LP relaxation.

Study of mathematical methods and algorithms in bioinformatics: Topics: Probability and statistics (prerequisites, statistical estimation, Markov chains, evolutionary models, sequence alignment), Hidden Markov models (Viterbi algorithm), Bayesian networks (principles, network inference), sequence alignment and phylogenetic trees (evolutionary relations, multiple sequence alignment, tree building).

Computational biology and bioinformatics aim at an understanding of living systems through computation. The seminar combines student presentations and current research project presentations to review the rapidly developing field from a computer science perspective. Areas: DNA sequence analysis, proteomics, optimization and bio-inspired computing, and systems modeling, simulation and analysis.

Computational biology and bioinformatics aim at an understanding of living systems through computation. The seminar combines student presentations and current research project presentations to review the rapidly developing field from a computer science perspective. Areas: DNA sequence analysis, proteomics, optimization and bio-inspired computing, and systems modeling, simulation and analysis.

The course provides the foundations for the design and analysis of algorithms.Classic problems ranging from sorting up to problems on graphs are used to discuss common data structures, algorithms and algorithm design paradigms.The course also comprises an introduction to parallel and concurrent programming and the programming model of C++ is discussed in some depth.

In this course, we will study the design and analysis of efficient external memory algorithms and data structures. Different paradigms for efficiently solving problems in external memory will be presented, and a number of specific algorithms from areas like sorting and searching, computational geometry, strings, and graphs will be covered.

We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques.

The course discusses algorithmic issues related to the Web, employing interesting algorithmic and mathematical techniques for modeling and analyzing various Web related problems w.r.t. network structure (small world, hotlink assignment, page ranking), basics of game theory, selfish agents, auctions, distributed selfish packet routing and load balancing, and on-line control in some generality.