MICRO-428: MetrologyThe course deals with the concept of measuring in different domains, particularly in the electrical, optical, and microscale domains. The course will end with a perspective on quantum measurements, which could trigger the ultimate revolution in metrology.
AR-219: Advanced CAO and Integrated Modeling DIM1ère année: bases nécessaires à la représentation informatique 2D (3D).
Passage d'un à plusieurs logiciels: compétence de choisir les outils adéquats en 2D et en 3D.
Mise en relation des outils de CAO et DIM: regard critique et aptitude à choisir les méthodes nécessaires au résultat recherché.
EE-552: Media securityThis course provides attendees with theoretical and practical issues in media security. In addition to lectures by the professor, the course includes laboratory sessions, a mini-project, and a mid-term exam.
MICRO-562: Biomicroscopy IIIntroduction to the different contrast enhancing methods in optical microscopy. Basic hands-on experience with optical microscopes at EPFL's BioImaging and Optics Facility. How to investigate biological samples? How to obtain high quality images?
ME-213: Programmation pour ingénieurMettre en pratique les bases de la programmation vues au semestre précédent. Développer un logiciel structuré. Méthode de debug d'un logiciel. Introduction à la programmation scientifique. Introduction à l'instrumentation virtuelle.
MICRO-561: Biomicroscopy IIntroduction to geometrical and wave optics for understanding the principles of optical microscopes, their advantages and limitations. Describing the basic microscopy components and the commonly used biomicrocopy methods such as widefield and fluorescence.
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.
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 images, music and social network data.