Objectives of Programme's Learning Outcomes

• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out geology and mining engineering missions, using their expertise and adaptability.
• Analyse and model a problem by critically selecting theories and methodological approaches (modelling, calculations), and taking into account multidisciplinary aspects.
• Work effectively in teams, develop leadership, make decisions in multidisciplinary, multicultural, and international contexts.
• Interact effectively with others to carry out common projects in various contexts (multidisciplinary, multicultural, and international).
• Communicate and exchange information in a structured way - orally, graphically and in writing, in French and in one or more other languages - scientifically, culturally, technically and interpersonally, by adapting to the intended purpose and the relevant public.
• Use and produce scientific and technical documents (reports, plans, specifications) adapted to the intended purpose and the relevant public.
• Adopt a professional and responsible approach, showing an open and critical mind in an independent professional development process.
• Exploit the different means available in order to inform and train independently.
• Contribute by researching the innovative solution of a problem in engineering sciences.
• Design and implement technical analysis, experimental studies and numerical modelling.

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 Mons AI Meetups launched in 2017).
They are also accessible to people registered to the Certificat d'Université en Intelligence Artificielle (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