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.


MSc in Informatics

08:30 - 10:30
C1.05, East Campus

Algorithms & Complexity
Prof. Evanthia Papadopoulou

Course objectives

Algorithms are fundamental to computer science and they lie at the core of any software system. This course will cover fundamental techniques for designing efficient computer algorithms, proving their correctness, and analyzing their performance. It will also cover several application problems that use these techniques. Students will encounter a variety of problems and techniques; the objective is to learn algorithmic foundations of computer science and acquire the ability to design correct algorithms on their own.

Course description

The course contents include graph traversals, greedy algorithms, divide and conquer algorithms, dynamic programming, network flow, bipartite matching, circulation, NP completeness and computational intractability, approximation algorithms, and (time permitting) randomized algorithms. Techniques on algorithm design and analysis will be developed by drawing on problems from across many areas of computer science and related fields.

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
D1.13, East Campus

Computational Biology and Drug Design
Prof. Vittorio Limongelli

Course objectives

Basic knowledge of chemistry Basic knowledge of biology and pharmacology Good knowledge of structure-based drug design Good knowledge of molecular simulations in pharmacology (drug/molecular target interaction).

Course description

The course provides knowledge to deal with calculations of biological interest. Principles of biology and chemistry are delivered together with a deep understanding of the methods used to compute chemico/physical properties of molecules such as organic and peptidic ligands, proteins and nucleic acids. Standard and advanced computational techniques are described in details and many applications illustrated. Molecular dynamics, free-energy calculations are some examples. Great attention is dedicated to the application of these methods in drug design through rational approaches and more automated protocols.

16:30 - 18:00
C1.04, East Campus

Machine Learning
Dr Michael Wand

Course objectives

Master's students will gain familiarity with state-of-the-art machine learning; focusing on neural networks and reinforcement learning.

Course description

Introductory Master's Course to Artificial Intelligence (AI), taught by experts of the award-winning Swiss AI Lab IDSIA. Machine Learning (ML) is both a cornerstone of AI and a top skill sought by IT employers. Today ML is everywhere: search engines use it to improve answers, email programs use it to filter spam, banks use it to predict stock markets, doctors use it to recognize tumors, robots use it to localize themselves and to understand their environment, video games use it to enhance the player's experience, smartphones use it to recognize objects / faces / gestures / voices / music, etc. This course covers both basic theory and challenging applications in the field, after a few lectures, students will already be able to train a neural network to recognize images better than with any other known method.