Ce cours est destiné aux étudiant-e-s du master MSE de la HES-SO

Ce cours est destiné aux étudiant-e-s du master MSE de la HES-SO

  • Today, Machine learning (ML) is the methodological driver behind the mega-trends of big data and data science. ML experts are highly sought after in industry and academia alike. 
  • This course builds upon basic knowledge in math, programming and analytics/statistics as is typically gained in respective undergraduate courses of diverse engineering disciplines. 
  • From there, it teaches the foundations of modern machine learning techniques in a way that focuses on practical applicability to real-world problems. The complete process of building a learning system is considered: 
    • Formulating the task at hand as a learning problem;
    • Extracting useful features from the available data;
    • Choosing and parameterising a suitable learning algorithm.
  • Covered Topics
    • cross-cutting concerns like ML system design and debugging (how to get intuition into learned models and results)
    • feature engineering
    • covered algorithms include (amongst others)
      • Bayesian approaches
      • Support Vector Machines (SVM)
      • Neural NetworksClustering
      • The emerging champion of ML methods, supervised and unsupervised deep learning techniques
      • … and many others.

Ce cours est destiné aux étudiant-e-s du master MSE à la HES-SO