Objectives of Programme's Learning Outcomes

• Imagine, design, develop, and implement conceptual models and computer solutions to address complex problems including decision-making, optimisation, management and production as part of a business innovation approach by integrating changing needs, contexts and issues (technical, economic, societal, ethical and environmental).
• On the basis of modelling, design a system or a strategy addressing the problem raised; evaluate them in light of various parameters of the specifications.
• Deliver a solution selected in the form of diagrams, graphs, prototypes, software and/or digital models.
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out computer and management engineering missions, using their expertise and adaptability.
• Master and appropriately mobilise knowledge, models, methods and techniques specific to computer management engineering.
• Analyse and model an innovative IT solution or a business strategy by critically selecting theories and methodological approaches (modelling, optimisation, algorithms, calculations), and taking into account multidisciplinary aspects.
• Identify and discuss possible applications of new and emerging technologies in the field of information technology and sciences and quantifying and qualifying business management.
• Assess the validity of models and results in view of the state of science and characteristics of the problem.
• Plan, manage and lead projects in view of their objectives, resources and constraints, ensuring the quality of activities and deliverables.
• Define and align the project in view of its objectives, resources and constraints.
• Respect deadlines and timescales
• 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.
• Construct a theoretical or conceptual reference framework, formulate innovative solutions from the analysis of scientific literature, particularly in new or emerging disciplines.
• Develop and implement conceptual analysis, numerical modelling, software implementations, experimental studies and behavioural analysis.
• Collect and analyse data rigorously.
• Adequately interpret results taking into account the reference framework within which the research was developed.
• Communicate, in writing and orally, on the approach and its results in highlighting both the scientific criteria of the research conducted and the theoretical and technical innovation potential, as well as possible non-technical issues.

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