Description

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. 

Finalité

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. 

Description des modalités d'évaluation

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

Public

• 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.).