Objectives of Programme's Learning Outcomes

• Carry out major projects.
• Work in teams and, in particular, communicate effectively and with respect for others.
• Present the objectives and results of a project orally and in writing.
• Communicate clearly.
• Communicate the results of mathematical or related fields, both orally and in writing, by adapting to the public.
• Adapt to different contexts.
• Have developed a high degree of independence to acquire additional knowledge and new skills to evolve in different contexts.
• Critically reflect on the impact of mathematics and the implications of projects to which they contribute.

Learning Outcomes of UE

See single learning activity.

Content of UE

This unit is a follow-up to the first course in machine learning. It aims to supplement and deepen knowledge in machine learning and data analysis.

Prior Experience

"Machine learning" is a pre-requisite for this unit.

Type of Assessment for UE in Q2

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

Q2 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

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