Objectives of Programme's Learning Outcomes

• Manage large-scale software development projects.
• Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
• Demonstrate independence and their ability to work alone or in teams.
• Manage research, development and innovation.
• Understand unprecedented problems in computer science and its applications.
• Organise and lead a research, development or innovation project to completion.
• 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.
• Pursue further training and develop new skills independently.
• Apply scientific methodology.
• Critically reflect on the impact of IT in general, and on the contribution to projects.
• Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

Get familiar with the contemporary methods in machine learning (active learning, reinforcement learning, depp networks) Study these methods within the frameworks of statistical learning theory

Content of UE

active learning, reinforcement learning, depp network statistical learning theory

Prior Experience

Not applicable

Type of Assessment for UE in Q2

• Presentation and/or works
• Written examination

Q2 UE Assessment Comments

Theoretical exam and presentation of a practical project on the computer  

Type of Assessment for UE in Q3

• Presentation and/or works
• Written examination

Q3 UE Assessment Comments

same as Q1