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.
• Lead a project by mastering its complexity and taking into account the objectives, allocated resources and constraints that characterise it.
• Demonstrate independence and their ability to work alone or in teams.
• 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.
• Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

Practical (hands-on) knowledge of the AI tools (mostly deep nets and deep reinforcement learning); knowledge og the state-of-the-art deep net architectures for solving AI problems.

Content of UE

Four applicative challenges in AI, coming from various domains are proposed. For each challenge, 3 hours are devoted to theory, followed by two 3-hours co-working sessions in teams, and a report is prepared by students at home.
A series of seminars are organized in parallel, on transdisciplinary topics related to AI. 
All activities are proposed in evenings (in the same format as the <a href="https://www.meetup.com/fr-FR/AI-Mons/">Mons AI Meetups </a>launched in 2017).
They are also accessible to people registered to the <a href="https://web.umons.ac.be/fpms/fr/formations/intelligence-artificielle-hands-on-ai/">Certificat d'Université en Intelligence Artificielle</a> (See this page for more info, especially the Programme and Structure tab)

Prior Experience

Basics of computer science and programming languages (Python)

Type of Assessment for UE in Q1

• Presentation and/or works

Q1 UE Assessment Comments

Challenge reports (one report per group per challenge), 80% Individual reports on seminars, 20%

Type of Assessment for UE in Q3

• Presentation and/or works

Q3 UE Assessment Comments

Challenge reports (one report per group per challenge), 80% Individual reports on seminars, 20%

Type of Resit Assessment for UE in Q1 (BAB1)

• N/A

Q1 UE Resit Assessment Comments (BAB1)

not applicable