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

13.05.2022  
8:30-10:15
Room C1.05
East Campus

Robotics
Prof. Giusti

The course theory part explores the following topics:

  • 2D and 3D pose representation and transformations
  • Workspace, C-Space, Degrees of Freedom
  • Kinematics for arms and wheeled robots
  • Feedback-based control
  • Sensors
  • Localization, Mapping, SLAM
  • Path planning

The practical part explores algorithm implementation with Jupyter Notebooks (good programming skills with Python are required) and ROS.

10:30-12:15
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.

12:30-14:15
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.

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

Events
30
June
2022
30.
06.
2022

Device Accelerated solvers with PETSc: current status, future perspectives, and applications

Faculty of Biomedical Sciences, Faculty of Economics, Faculty of Informatics
01
July
2022
01.
07.
2022

Scalable Gaussian Processes

Faculty of Informatics
06
July
2022
06.
07.
2022
07
July
2022
07.
07.
2022