In the CSE Case Studies Seminar invited speakers from ETH, from other universities as well as from industry give a talk on an applied topic. Beside of attending the scientific talks students are asked to give short presentations (10 minutes) on a published paper out of a list.

In the CSE Case Studies Seminar invited speakers from ETH, from other universities as well as from industry give a talk on an applied topic. Beside of attending the scientific talks students are asked to give short presentations (10 minutes) on a published paper out of a list.

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.

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

Numerical Quadrature: Methods of numerical integration, Euler-Mac Laurin summation. Ordinary differential equations: discretization, error analysis, multistep methods, Runge-Kutta methods, adaptive methods. Numerical Differentiation: numerical derivatives by finite differencing, algorithmic differentiation. Introduction to Partial Differential Equations.

Introduction to linear algebra: vectors and matrices, solving systems of linear equations, vector spaces and subspaces, orthogonality and least squares, determinants, eigenvalues and eigenvectors, singular value decomposition and linear transformations. Applications in and links to computer science will be presented in parallel.

The aim of this course is to show how numerical algorithms are implemented correctly and efficiently.We follow this agenda by discussing various important algorithms of numerical linear algebra.