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-407: Frontiers in nanosciencesThe course covers relevant experimental and theoretical concepts in nanoscale science, from fundamental aspects like quantum tunneling and quantum size effects, to hot topics like quantum transport and nanoscale magnetism. ...
MSE-352: Introduction à la microscopie + TPCe cours d'introduction à la microscopie a pour but de donner un apperçu des différentes techniques d'analyse de la microstructure et de la composition des matériaux, en particulier celles liées aux microscopies électronique et optique. Le cours comprend d ...
ME-324: Commande numérique des systèmes dynamiquesOn introduit les bases de l'automatique linéaire discrète qui consiste à appliquer une commande sur des intervalles uniformément espacés. La cadence de l'échantillonnage qui est associée joue un rôle primordial pour assurer la stabilité et la performance d ...
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. ...
EE-442: Wireless receivers: algorithms and architecturesThe students will learn about the basic principles of wireless communication systems, including transmission and modulation schemes as well as the basic components and algorithms of a wireless receiver. They develop an understanding for the wireless channe ...
MATH-522: Empirical processesWe learn how to control the nonasymptotic and random behavior of collections of estimators, when indexed by classes of functions/sets. Examples range from prototypical estimators used by statisticians, to nonparametric models used in machine learning. ...
MSE-637(b): Transmission electron microscopy and diffraction (b)This intensive course is intended for researchers who envisage using transmission electron microscopy to study materials samples or to help them interpret TEM data in publications. It presents basics of TEM instrumentation, imaging, electron diffraction, s ...
BIO-449: Understanding statistics and experimental designThis course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental design and to avoid common pitfalls of statistical reason ...
COM-516: Markov chains and algorithmic applicationsThe study of random walks finds many applications in computer science and communications. The goal of the course is to get familiar with the theory of random walks, and to get an overview of some applications of this theory to problems of interest in commu ...
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.) ...
MATH-101(f): Analyse IÉtudier les concepts fondamentaux d'analyse et le calcul différentiel et intégral des fonctions réelles d'une variable. ...
CH-419: Protein mass spectrometry and proteomicsIn systems biology, proteomics represents an essential pillar. The understanding of protein function and regulation provides key information to decipher the complexity of living systems. Proteomic technology now enables deep quantitative proteome mapping v ...
CH-633: Advanced Solid State and Surface CharacterizationState-of-the-art surface/thin film characterization methods of polycrystalline/nano/amorphous materials. Selected topics from thin film X-ray diffraction (GIWAXS, GISAXS, PDF), electronic and optical spectroscopy (XPS, AES, SERS, TERS), scanning probe and ...
CS-455: Topics in theoretical computer scienceThe students gain an in-depth knowledge of several current and emerging areas of theoretical computer science. The course familiarizes them with advanced techniques, and develops an understanding of fundamental questions that underlie some of the key probl ...
MSE-213: Probability and statistics for materials scienceThe course establishes the concepts of statistical analysis, probability theory, and data analysis. This includes standard statistical tests, correlation analysis and experimental design. It introduces computational statistical methods to analyse large dat ...
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 ...
