Objectives of Programme's Learning Outcomes

• Provide clear and accurate information.
• Share their knowledge and findings clearly and back them up rationally to specialist and non-specialist audiences.
• Have a creative and rigorous scientific approach
• Gather and interpret relevant scientific data and critically analyse it, distinguising working hypotheses of proven facts.

Learning Outcomes of UE

This course provides a broad introduction to (statistical) machine learning. Topics include the learning framework (training and test errors, model assessment and selection, bias/variance tradeoff, resampling methods, regularization), supervised learning (linear models, tree-based models, neural networks, parametric/non-parametric models), and unsupervised learning (dimensionality reduction).

UE Content: description and pedagogical relevance

See the single learning activity.
 

Prior Experience

Basics of probability and statistics.
Basics of matrix algebra.
Basics of non-linear optimization.