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

Registration is compulsory. Please register online.

23.11.2021

MSc in Computational Science

08:30 - 10:00
C1.03, East Campus

Distributed Algorithms
Prof. Fernando Pedone

Course objectives
Distributed computing systems arise in a wide range of modern applications. This course surveys the foundations of many distributed computing systems, namely, the distributed algorithms that lie at their core. The course provides the basis for designing distributed algorithms and formally reasoning about their correctness. It addresses issues related to what distributed systems can and cannot do (i.e., impossibility results) in certain system models.

Course description
The course focuses on three aspects of distributed computing: system models, fundamental problems in distributed computing, and application of distributed algorithms. System models include synchronous versus asynchronous systems, communication models, and failure models. Several fundamental problems are covered, including consensus, atomic broadcast, atomic multicast, atomic commit, and data consistency. Applications of distributed algorithms target various forms of replication techniques.

10:30 - 12:00
D1.14, East Campus

High-Performance Computing
Prof. Olaf Schenk

Course objectives
Are you interested in using Europe’s faster supercomputers (and getting ECTS credit points for doing so)? Would you like to learn how to write programs for parallel supercomputers, such as a Cray or a cluster of GPUs? The course is designed to teach students how to program parallel computers to efficiently solve challenging problems in science and engineering, where very fast computers are required either to perform complex simulations or to analyze enormous datasets.

Course description
The goal of the HPC course is that students will learn principles and practices of basic numerical methods and HPC to enable large-scale scientific simulations. This goal will be achieved within six to eight mini-projects with a focus on HPC, CSE, and AI. The content of the course is tailored for 1st year Master students interested in both learning parallel programming models, scientific mathematical libraries, and having hands-on experience using HPC systems.

14:30 - 16:00
C1.04, East Campus

Introduction to Data Science
Prof. Ernst-Jan Camiel Wit

Course objectives

  • apply estimation procedures
  • derive properties, such as bias, consistency, sufficiency, efficiency, of estimation procedures;
  • show the proof for the Cramer-Rao lower bound and the Asymptotic efficiency of MLE;
  • derive and apply maximum likelihood, method-of-moments and Bayesian estimation;
  • apply Bayesian computational approaches;
  • apply hypothesis testing and derive its properties;
  • apply estimation and testing principles to linear regression.

Course description
In inductive practice we are interested to learn about the state of the world given some event, i.e., the data. In this course we will learn about ``estimation'' procedures, in particular maximum likelihood and the method of moments, and some of their theoretical properties. We also learn about hypothesis testing. Then we apply both estimation and testing to a practical setting: linear regression analysis. The course will be offered online as well to also allow double-degree Master students to enroll.

16:30 - 18:00
C1.05, East Campus

Numerical Algorithms
Prof. Kai Hormann

Course objectives
This course brings fundamental mathematical concepts to life by studying concrete examples of important everyday problems and explaining how they are solved by numerical algorithms. The students will understand the theoretical background of these methods, learn how to implement them, and experience the practical aspects.

Course description
We cover some key numerical algorithms for real-world applications, like GPS localization, TrueType fonts, robotic motion, Google’s PageRank, and JPEG compression. After going through some preliminary basics (Newton's root finding method, direct and iterative methods for solving linear systems, polynomial interpolation), we are ready to discuss the algorithms above, which are based on the concepts of least squares, Bézier curves, quadrature, eigenvalues, and the discrete cosine transformation, respectively.