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