Objectives of Programme's Learning Outcomes

• Understand the fundamentals of computer science
• Show an understanding and deep knowledge of the concepts of computer science and mathematical formalisms used in the field of computer science
• Solve exercises and computer problems by applying basic knowledge in the various disciplines of computer science
• Use the vocabulary and the correct mathematical reasoning to formulate and solve problems in the field of computer science
• Use and combine knowledge from different disciplines to solve multidisciplinary problems
• Manage IT projects
• Manage a project in compliance with specifications, constraints and deadlines
• Demonstrate independence and their ability to work in teams.
• Understand the fundamentals related to scientific methods
• Develop skills of abstraction and modelling through a conceptual and scientific approach
• Conduct rigorous reasoning based on scientific arguments
• Understand the fundamentals of communication
• Communicate information (both orally and in writing) relating to the field of computer science in an intelligible, clear and structured way
• Communicate a consistent and rigorous scientific argument, either orally or in writing
• Have a good command of language and communication techniques.

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