This course offers a comprehensive exploration of software engineering principles specifically adapted for artificial intelligence (AI) applications. It covers the full software development lifecycle (SDLC) of AI systems, including requirements engineering, design patterns for machine learning workflows, and software architecture for intelligent systems. Emphasis is placed on modern machine learning operations (MLOps) practices, such as automated training and deployment pipelines, model monitoring and performance evaluation, model versioning, and lifecycle management. The course also addresses responsible AI development, focusing on fairness, bias mitigation, and explainability, equipping students with the tools and methodologies needed to build robust, scalable, and ethical AI-powered software solutions.

Temps présentiel : 30 heures

Charge de travail étudiant : 70 heures

Méthode(s) d'évaluation : Examen final, Examen partiel, Travail personnel