Description

This course introduces students to the basics of artificial intelligence (AI) and its applications in mechanics. Topics covered include:

• Fundamentals of AI:
  • Linear Regression
  • Logistic Regression
  • Decision Tree Learning
  • Support Vector Machine (SVM)
  • k-Nearest Neighbors (k-NN)
  • Vanilla Gradient Descent
• Neural Networks and Deep Learning:
  • Artificial Neural Networks & Deep Learning
  • Recurrent Neural Networks (RNN) & Long Short-Term Memory (LSTM) methodology

Application
Students will apply these AI methods to implement and train models  in mechanical engineering fields, exploring how AI can enhance problem-solving in mechanics and optimization.

Compétences visées

By the end of the course, students will be able to:

• Understand and implement fundamental AI algorithms in mechanical applications.
• Develop neural network models and apply deep learning techniques.
• Train AI models to solve engineering problems in the mechanical domain.

Bibliographie

In fluid dynamics: Machine Learning for Fluid Mechanics, Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning,  Machine Learning Control,  Steven L. Brunton et al.

Contacts

Responsable(s) de l'enseignement

• Abderahmane Marouf

MCC

Les épreuves indiquées respectent et appliquent le règlement de votre formation, disponible dans l'onglet Documents de la description de la formation.

Régime d'évaluation

CT (Contrôle terminal, mêlé de contrôle continu)

Coefficient

2.0

Évaluation initiale / Session principale - Épreuves