Master Meetings

Have you decided on which Master programme to study? Would you like more information on the contents and teaching methods at USI? Register at our Master Meetings to attend courses.
The various Master Meetings offer you the opportunity to follow lectures together with the current master students. Guided by a USI student, you can visit the campus and make up your mind as to whether the contents correspond to your study ambitions

Next appointment: 9-13 May 2022. Registration is required. 

Registration form

Room D1.13
East Campus

Introduction to Partial Differential Equations
Prof. Multerer

Many phenomena in real life applications are modeled by partial differential equations (PDEs). These mathematical models are sets of equations, which describe the essential behavior of a natural or artificial system, in order to forecast and control its evolution. We will give an overview on the derivation of PDEs from physical applications and discuss their mathematical background. The theoretical investigations will be accompanied by the introduction and implementation of numerical schemes for their actual solution. In this course, we will mainly focus on elliptic and parabolic PDEs.

Room D1.13
East Campus

Geometric Algorithms
Prof. Papadopoulou

This course is an introduction to computational geometry and its applications. Computational geometry is well related to many application domains, such as pattern recognition, image processing, computer graphics, robotics, geographic information systems (GIS), computer-aided design (CAD), information retrieval, computational science, and others. The students will learn fundamental algorithmic techniques and practice in designing algorithms of their own.

Room D1.13
East Campus

Solution and Optimization Methods for Large Scale Problems
Prof. Krause

Large scale systems and large scale optimization problems are of central importance in computational science, optimization, and machine learning. Since standard solution and minimization methods in general do not scale optimally, alternative solution strategies have been developed during the last decades. In particular hierarchical solution strategies and parallel strategies have been developed. Prominent examples are multilevel or domain decomposition methods, originally developed for linear elliptic problems. We start from basic iterative methods, and then consider Krylov-space methods and eventually subspace correction methods for linear and non-linear problems,. We will discuss multilevel optimization methods such as MG/OPT, (recursive) trust-region methods (RMTR) and hierarchical minimization methods for machine learning, including variance reduction methods.

Room D1.13
East Campus

Graphical Models and Network Science
Prof. Wit

This course is an introduction to the statistical modeling of networks. Emphasis will be on statistical methodology and subject-matter-agnostic models, rather than on the specifics of different application areas. The course will deal with complex stochastic interaction models that can be used to describe specific dynamics of well-defined systems or more parsimonious models to explore the interaction structure of large systems. 

In the Autumn Semester 2021, prospective students joined the classes: