Objectives of Programme's Learning Outcomes

• Imagine, design, implement and operate compounds, products and materials to specific properties and physical, chemical and biochemical solutions/processes leading to obtaining these materials by integrating needs, contexts and issues (technical, economic, societal, ethical, safety and environmental).
• Implement a chosen solution/product in the form of a drawing, a schema, a plan, a model, a prototype, software and/or digital model.
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out missions of chemical engineering and materials science, using their expertise and adaptability.
• Analyse and model a problem/process/producing pathway by critically selecting theories and methodological approaches (modelling, calculations), and taking into account multidisciplinary aspects.
• Identify and discuss possible applications of new and emerging technologies in the field of chemistry and materials science.
• Plan, manage and lead projects in view of their objectives, resources and constraints, ensuring the quality of activities and deliverables.
• Respect deadlines and the work plan, and adhere to specifications.
• 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, etc.) 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.
• Acquire and analyse data rigorously.
• Adequately interpret the results taking into account the reference framework within which the research was developed.

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