MSE Master of Science in Engineering

The Swiss engineering master's degree

Chaque module vaut 3 ECTS. Vous sélectionnez 10 modules/30 ECTS parmi les catégories suivantes:

  • 12-15 crédits ECTS en Modules technico-scientifiques (TSM)
    Les modules TSM vous transmettent une compétence technique spécifique à votre orientation et complètent les modules de spécialisation décentralisés.
  • 9-12 crédits ECTS en Bases théoriques élargies (FTP)
    Les modules FTP traitent de bases théoriques telles que les mathématiques élevées, la physique, la théorie de l’information, la chimie, etc., vous permettant d’étendre votre profondeur scientifique abstraite et de contribuer à créer le lien important entre l’abstraction et l’application dans le domaine de l’innovation.
  • 6-9 crédits ECTS en Modules contextuels (CM)
    Les modules CM vous transmettent des compétences supplémentaires dans des domaines tels que la gestion des technologies, la gestion d’entreprise, la communication, la gestion de projets, le droit des brevets et des contrats, etc.

Le descriptif de module (download pdf) contient le détail des langues pour chaque module selon les catégories suivantes:

  • leçons
  • documentation
  • examen 
Machine Learning in Computer Vision (TSM_CompVis)

Analyzing images is a very complex task that has many important real-world applications.  This module presents powerful techniques to extract information from images and 3D data, based on machine learning and deep learning methods.  These methods are mostly used as “black boxes” and their inner workings are not discussed in much detail. The module provides an overview of many image analysis applications such as document analysis, medical imaging and autonomous driving; examples of advanced uses of deep learning on images (generative networks for image synthesis, adversarial networks, neural style transfer) are also discussed.

Compétences préalables


  • Basic knowledge of machine learning (e.g. Andrew Ng’s ML course on Coursera)
  • Good command of an imperative programming language, basic knowledge of Python (the module will use Python 3).
  • Sections 1.1, 1.2, 1.3, 3.6.1, 3.6.2
  • Basic knowledge of probability, statistics, linear algebra (vectors, matrices)
  • Students are expected to take their laptops for the Lab activities

Objectifs d'apprentissage

  • Students know how images and 3D data are represented and manipulated by software
  • Students know the most important problems related to image analysis: e.g. image classification, segmentation and object detection and tracking
  • Students can apply machine learning and deep learning techniques to solve image-related problems, and deal with practical issues arising in the field (dataset engineering, data augmentation, data normalization)
  • Students have seen different examples of image analysis problems and common solution techniques, and are able to acquire additional expert knowledge from the scientific literature and online resources

Catégorie de module

  • Introduction
  • Basic image processing methods applied to document processing: binarization; segmentation of text into lines, words and characters; connected component analysis.
  • Image classification
    • applications to OCR: handcrafted features; convolutional neural networks.
    • Image classification with small datasets: data augmentation techniques; one-shot learning; transfer learning and pre-trained models.
  • Segmentation
    • applications to medical images (2D, 3D)
    • fully convolutional networks for semantic segmentation.
  • Object detection
    • face detection with cascading classifiers
    • pedestrian detection for autonomous driving
    • object tracking in videos.
  • Generative models and Image Synthesis
    • Applications to Image Inpainting;
    • Generative Adversarial Networks;
    • Neural style transfer.

Méthodes d'enseignement et d'apprentissage

Classroom teaching; programming exercises using python and frameworks in python


  • Computer Vision: Algorithms and Applications, Richard Szeliski, 2010
  • Deep Learning with Python, Francois Chollet, early 2018, Sections 5, 8.3, 8.5

Télécharger le descriptif complet