Comprendre et appliquer les fondamentaux de l'intelligence artificielle

Aperçu

By the end of this course, learners will be able to explain core artificial intelligence concepts, analyze intelligent reasoning methods, apply machine learning techniques, and evaluate reinforcement learning approaches used in real-world AI systems. This course provides a comprehensive and structured introduction to artificial intelligence, guiding learners from foundational concepts to practical learning paradigms.

It begins by establishing a clear understanding of what artificial intelligence is, how it has evolved, and why it matters, while addressing ethical and societal considerations that shape responsible AI development. Learners then explore the logical, probabilistic, and search-based reasoning techniques that enable intelligent decision-making.

The course advances into machine learning, covering supervised and unsupervised learning, clustering, distance measures, dimensionality reduction, and association rule learning. It culminates with reinforcement learning, where learners examine how intelligent agents learn through interaction, rewards, and feedback using both model-based and model-free approaches.

What makes this course unique is its end-to-end learning journey, combining conceptual clarity, theoretical foundations, and applied machine learning perspectives within a single cohesive structure. Upon completion, learners will gain practical AI literacy, critical thinking skills, and a strong foundation for advanced AI, data science, or machine learning studies.

Programme

• Fondements de l'Intelligence Artificielle et Raisonnement Intelligent
Ce module présente les concepts fondamentaux de l'intelligence artificielle, y compris son évolution historique, ses implications éthiques, et les mécanismes de raisonnement clés qui permettent aux systèmes intelligents de résoudre des problèmes et de prendre des décisions dans des environnements incertains.
• Techniques d'Apprentissage Automatique et Systèmes Adaptatifs
Ce module se concentre sur les paradigmes d'apprentissage automatique et les systèmes adaptatifs, couvrant l'apprentissage supervisé et non supervisé, les techniques de représentation de données, et les approches d'apprentissage par renforcement qui permettent aux agents intelligents d'apprendre à partir des données et de l'interaction.

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Enseigné par

EDUCBA

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Matières

Artificial Intelligence