PHYS-512: Statistical physics of computationThe students understand tools from the statistical physics of disordered systems, and apply them to study computational and statistical problems in graph theory, discrete optimisation, inference and machine learning. ...
PHYS-439: Introduction to astroparticle physicsWe provide a comprehensive overview of astroparticle physics, bridging the microworld of cosmic messengers with the large-scale structure and evolution of the Universe. We also explore the detection techniques and technologies that make this investigation ...
PHYS-467: Machine learning for physicistsMachine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised. ...
PHYS-428: Relativity and cosmology IIThis course is the basic introduction to modern cosmology. It introduces students to the main concepts and formalism of cosmology, the observational status of Hot Big Bang theory
and discusses major physical processes in the early Universe. ...
CS-401: Applied data analysisThis course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.) ...
CS-421: Machine learning for behavioral dataComputer environments such as educational games, interactive simulations, and web services provide large amounts of data, which can be analyzed and serve as a basis for adaptation. This course will cover the core methods of user modeling and personalizatio ...
ME-427: Networked control systemsThis course offers an introduction to control systems using communication networks for interfacing sensors, actuators, controllers, and processes. Challenges due to network non-idealities and opportunities offered by communication will be analyzed. ...
PHYS-642: Statistical physics for optimization & learningThis course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction to inference to machine learning, neural networks and st ...
ENV-341: Remote sensingCe cours a pour objectif de familiariser les étudiants avec les principaux concepts, instruments et techniques de la télédétection environnementale. Les interactions ondes/matière, les différents types instruments et les techniques de traitements d'images ...
PHYS-643: Astrophysics VI : The variable UniverseIntroduction to time-variable astrophysical objects and processes, from Space Weather to stars, black holes, and galaxies. Introduction to time-series analysis, instrumentation targeting variability, and the importance of the time domain for astronomy, cos ...
CS-423: Distributed information systemsThis course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems. ...
DH-406: Machine learning for DHThis course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as imag ...
PHYS-435: Statistical physics IIIThis course introduces statistical field theory, and uses concepts related to phase transitions to discuss a variety of complex systems (random walks and polymers, disordered systems, combinatorial optimisation, information theory and error correcting cod ...
PHYS-209: Astrophysics I: introduction to astrophysicsCe cours décrit de façon simple les processus physiques qui expliquent l'univers dans lequel nous vivons. En couvrant une large gamme de sujets, le but du cours est aussi de donner un aperçu général des objets astrophysiques qui nous entourent. ...
CS-233(a): Introduction to machine learning (BA3)Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented. ...
