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: 21-25 November 2022.

Registration form


Room D1.14
East Campus

Artificial Intelligence

The aim of the course is to present the most modern techniques for solving complex problems. We focus on state of the art meta-heuristic for continuous function and combinatorial optimization: among the methods we deepen simulated annealing, genetic algorithms, variable neighborhood search and ant colony optimization. Gaming with two players will be also discussed as the integration between meta-heuristics and machine learning. Students are asked to implement and test some of these techniques.

Room C1.03
East Campus

Deep Learning Lab

This course will introduce students to practical implementations of various deep learning models using Python and the PyTorch library. Recommended lectures are: Machine Learning, and basic courses on Linear Algebra, Analysis, Probability & Statistics. Basic knowledge of Python is expected, but it is not a hard requirement as long as the student is capable of learning it quickly.

Room D1.15
East Campus

Introduction to Data Science

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.

Room D1.14
East Campus

Efficient Computational Algorithms

This course provides a comprehensive overview of the concepts of algorithm analysis and computing development. We will review 14 computing algorithms with the greatest influence on the development and practice of computing and engineering in the 20th century. We consider the following list of algorithms: Metropolis Algorithm for Monte Carlo, Simplex Method for Linear Programming, Krylov Subspace Iteration Methods, The Decompositional Approach to Matrix Computations, QR Algorithm for Computing Eigenvalues, Quicksort Algorithm for Sorting, Fast Fourier Transform, Integer Relation Detection, Fast Multipole Methods, Gradient Descent and Stochastic Gradient Descent, Randomized Low-rank Approximation, Sparse Grids, Hierarchical Matrices and Wavelets.

Room C1.05
East Campus

Machine Learning

Introductory Master's Course to Machine Learning (ML), which is both a cornerstone of Artificial Intelligence (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 basic and advanced theory and methods of Machine Learning. From this wide field, we focus on neural networks, probabilistic models, and reinforcement learning in both theory and practice. Students will solve theoretical exercises and perform programming tasks; after just a few lectures, they will be able to implement a neural network which performs image classification better than any other known method. The intention of this course is to lay a solid groundwork for the student, such that he/she will be able to understand advanced state-of-the-art methods, to skillfully use diverse methods to solve practical problems, and to properly interpret results.