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 goals
- 1. S’approprier les usages avancés et spécialisés des outils de l’intelligence digitale en s’assurant de leur impact durable et responsable
Learning objectives
- 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.
Course Learning objectives
By the end of the course, each student will be able to engage in context engineering work (non-technical or low-technical) that fits their job and personal life needs. Such works could be in forms of custom chatting bots, knowledge base for AI conversation threads, using local folder as an agentic AI application, etc.
Content : structure and schedule
Session 1 - From Symbolic Rules to World Models
Session 2 - Traditional ML Methods
Session 3 - Towards Bionic Approach: ANN & Transformer
Session 4 - Agentic Intelligence & Psychological Implications
Session 5 - Wrap-up & Free Discussion
Session 6 - Exploring AI Tools Across Layers
Session 7 - Harness LLM
Session 8 - AI Safety & Project Follow-Up
Session 9 - Individual Project Check - Ideas, Trials & Errors, etc.
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
Learning delivery
Mixte
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 projects ranging from easy to intermediate, understanding, use, and application of AI, each weighing 25% of total score (1 group project, 3 individual projects). The grading is a mix of peer assessment and instructor assessment. Students who fail the course will have another chance to take a catch-up exam (CF2), which is in the form of a critical case analysis online (under controlled environment). No MCQ is involved in any assessments.
Keywords
Artificial Intelligence, LLM, Agentic AI