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

The course introduces statistical and machine learning methods for predictive modelling based on big datasets. The unit covers, among other topics, linear and non-linear methods for regression, classification, clustering and dimensionality reduction. 

Content of UE

See the single learning activity.
 

Prior Experience

Basics of Probability and Statistics
Basics of Matrix Algebra
Basics of Non-linear optimization

Type of Assessment for UE in Q2

• Presentation and/or works
• Written examination
• Graded tests

Q2 UE Assessment Comments

Written exam (60% of total score)
Project (20% of total score)
Assignments (20% of total score)
There is a hurdle of 50% for each of the previous evaluations

Type of Assessment for UE in Q3

• Presentation and/or works
• Oral examination
• Graded tests

Q3 UE Assessment Comments

Oral exam (60% of total score)
Project (20% of total score)
Assignments (20% of total score)
There is a hurdle of 50% for each of the previous evaluations