Objectives of Programme's Learning Outcomes

• Manage research, development and innovation.
• Understand unprecedented problems in computer science and its applications.
• Master communication techniques.
• Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
• Develop and integrate a high degree of autonomy.
• Aquire new knowledge independently.
• Apply scientific methodology.
• Critically reflect on the impact of IT in general, and on the contribution to projects.

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