Applied Artificial Intelligence
Code UE : USEEU6
- Cours
- 3 crédits
Responsable(s)
Stefano SECCI
Public, conditions d’accès et 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 pédagogiques
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.
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.
Modalité d'évaluation
Project work; a project assignment to perform after the STC execution will also be evaluated.
Cette UE apparaît dans les diplômes et certificats suivants
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Code UE : USEEU6
- Cours
- 3 crédits
Responsable(s)
Stefano SECCI
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