Code
MUFE INF 3405
Level
L3
Field
Informatique
Language
Anglais/English
ECTS Credits
3
Class hours
18
Total student load
60
Program Manager(s)
Department
- Technologies, Information et Management
Educational team
Introduction to the module
This course introduces students to the conceptual, technical, and societal foundations of Artificial Intelligence, emphasizing how intelligent systems learn, decide, and act based on a combination of design and emergent features. It bridges machine learning paradigms—supervised, unsupervised, and reinforcement learning—with managerial and behavioral insights relevant to business and digital transformation. Through a combination of conceptual lectures, case analyses, and hands-on exercises using tools like Google Colab, students will explore how AI models such as neural networks, transformers, and large language models function, as well as their implications for creativity, cybersecurity, and ethical governance. The course invites critical reflection on the nature of intelligence, data, and truth, encouraging students to connect computational mechanisms with human cognition and decision-making in the emerging AI-driven world.
Learning objectives/Intended learning outcomes
- 1.1 - Audit advanced and specialised uses of digital intelligence tools in order to deploy them appropriately, taking into account the strategic context of organisations.
- 4.4 - Raise awareness and promote the application of ethical principles, professional conduct and environmental responsibility, in a context of organisational and societal transformation, and for the common good.
Sustainable Development Goals
In Fundamentals of AI, I contribute to SDG 10 (Reduced Inequalities) and SDG 12 (Responsible Consumption and Production) by teaching students to evaluate AI systems critically rather than adopt them blindly. The course highlights how AI can amplify inequalities through biased data, unequal access, and automation errors, and trains students to recognize these risks. Through hands-on cases and evaluation exercises, students learn to choose appropriate AI uses, question model outputs, and design responsible AI-enabled workflows that reduce harm, waste, and irresponsible deployment in organizations.
Number of SDG's addressed among the 17
10, 12
Pedagogical methods
Case-based learning; Problem-based learning; Group work & collaborative projects; Peer feedback and peer review; Portfolio-style assessment
Evaluation and grading system and catch up exams
Four practical projects ranging from easy to intermediate use and application of AI, each weighing 25% of total score. However, if students show a lack of conceptual understanding of AI, a written exam or theoretical presentation project may replace one of the four practical projects. Students who fail the course at the end will have another chance to take a catch-up exam, which is in the form of a critical case analysis.