Data analysis and applied machine learning
7.5 ECTS credits- Introduction to machine learning
- Data management and pre-processing
- Supervised learning, such as regression models and classification techniques
- Unsupervised learning, such as k-means and hierarchical clustering
- Foundations of neural networks and deep learning
- Applied ML within the field of engineering
- Ethics and responsible AI
Progressive specialisation:
A1N (has only first‐cycle course/s as entry requirements)
Education level:
Master's level
Admission requirements:
Mathematics 22.5 credits, Introduction to programming (7.5 credits), Scientific programming (7.5 credits) or Numerical methods (7.5 credits), plus upper secondary level English 6 or English level 2. An equivalence assessment can be made.
Selection:
Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.
This course is included in the following programme
- Master of Science in Mechanical Engineering (studied during year 5)