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Applied Artificial Intelligence

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Responsable(s) : M. Stefano SECCI

  • Cours
Code Cnam : USEEU6

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  • Durée : 30 heures
  • Package
  • 3 crédits

Présentation

Public, conditions d'accès et prérequis

Prérequis

  • Working knowledge of Python. 
  • Working knowledge of standard Python ML/DL libraries (sklearn, pytorch). 
  • Understanding of core ML/DL concepts (model, methods, training, performance evaluation, overfitting etc.). 

Objectifs

This course introduces students to the practical applications of artificial intelligence (AI) across various industrial domains. Through a combination of lectures, hands-on projects, and case studies, students will gain the knowledge and skills necessary to develop and deploy AI solutions to solve real-world problems. Topics covered will include AI models and methods, practices for operating ML-powered solutions, usage of LLMs and ethical considerations in AI. 

Compétences et débouchés

Parcours

Programme

Contenu

The course covers the following topics: 

  • Introduction to Applied Artificial Intelligence 
    • Overview of AI applications in different industries. 
    • Ethical considerations and responsible AI practices. 
  • Brief recap: Foundations of Machine Learning/Deep Learning 
    • Supervised, unsupervised, and reinforcement learning. 
    • Classification, regression, forecasting. 
    • Training, fine tuning and overfitting. 
    • Performance evaluation of ML/DL models. 
  • Domains: computer vision, natural language processing, sequential data. 
  • AI Deployment and Integration 
    • Model deployment strategies. 
    • Introduction to cloud-based AI services. 
    • Integrating AI models into applications and systems. 
  • Case Studies and Project Work 
    • Analysis of real-world AI applications across industries. 
    • Team project: Design and implementation of an AI solution for a specific use case. 
  • Project Presentation and Wrap-Up. 
    • Final project presentations by student groups. 
    • Reflection on key learnings and future directions in applied AI. 

Modalités d'évaluation

Project work; a project assignment to perform after the STC execution will also be evaluated.