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).

Study of computational techniques, algorithms and data structures used to solve problems in computational biology. Topics: basic biology, string alignment, phylogeny (distance, character, parsimony), molecular evolution, multiple sequence alignment, probabilistic and statistical models, Markov models, microarrays, dynamic programming, maximum likelihood and specialized DNA and protein 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.

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.

Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory.

Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory.

Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory.

The courses "Foundations of Computer Science" cover material that all students of computer science should know. The courses are self study courses and based on material which we assume that students know from their Bachelor program. The main aim of these courses is to ensure that all our Master students have a solid knowledge all over computer science and not just in their area of expertise.

The courses "Foundations of Computer Science" cover material that all students of computer science should know. The courses are self study courses and based on material which we assume that students know from their Bachelor program. The main aim of these courses is to ensure that all our Master students have a solid knowledge all over computer science and not just in their area of expertise.

The courses "Foundations of Computer Science" cover material that all students of computer science should know. The courses are self study courses and based on material which we assume that students know from their Bachelor program. The main aim of these courses is to ensure that all our Master students have a solid knowledge all over computer science and not just in their area of expertise.

The courses "Foundations of Computer Science" cover material that all students of computer science should know. The courses are self study courses and based on material which we assume that students know from their Bachelor program. The main aim of these courses is to ensure that all our Master students have a solid knowledge all over computer science and not just in their area of expertise.

The courses "Foundations of Computer Science" cover material that all students of computer science should know. The courses are self study courses and based on material which we assume that students know from their Bachelor program. The main aim of these courses is to ensure that all our Master students have a solid knowledge all over computer science and not just in their area of expertise.

Non-linear equations, Fundamentals of interpolation (points and functions), Nonlinear Least Squares, Optimization, Introduction to Symbolic computation.

Problem oriented course in scientific computing with emphasis onoptimization and modeling.linear and nonlinear least squares,sensitivity analysis,constraint minimization (Lagrange multipliers),conjugate gradient method,SVD,Linear programming,support vector classification,variational calculus,linear filter theory,nonlinear diffusion,dynamic programming,parsimony

Problem oriented course in scientific computing with emphasis onoptimization and modelling:Linear and nonlinear least squares, sensitivity analysis, constraint minimization (Lagrange multipliers) , conjugate gradient methodSVD, Linear programming, support vector classification, variational calculus, linear filter theory (Wiener filter), nonlinear diffusion, dynamic programming, parsimony.