Génie industriel - Rubrique Formation - 2022

Data science - 4GMA1625

  • Number of hours

    • Lectures 7.5
    • Projects -
    • Tutorials 7.5
    • Internship -
    • Laboratory works 6.0
    • Written tests 2.0

    ECTS

    ECTS 3.0

Goal(s)

This course aims to provide an understanding of data analysis, machine learning, neural networks, advanced deep learning and Large Language Models (LLM). Students will learn to master essential Python tools and apply this knowledge through independent practical work. The aim is to enable students to perform exploratory analyses, implement machine learning and deep learning models, and integrate LLMs to solve specific problems.

Responsible(s)

Nicolas CATUSSE

Content(s)

  • Data analysis
  • Machine learning
  • Neural networks
  • Deep learning
  • LLM

Prerequisites

  • Probability courses
  • Statistics course
  • Computer courses with Python

Test

CC = Tests during the semester
E1 = Final Exam, two-hour
N1 = Course grade

Calendar

The course exists in the following branches:

  • Curriculum - Engineer IPID apprentice program - Semester 7
see the course schedule for 2025-2026

Additional Information

Course ID : 4GMA1625
Course language(s): FR

You can find this course among all other courses.

Bibliography

  • Livres et Articles :
    - "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" par Aurélien Géron.
    - "Deep Learning" par Ian Goodfellow, Yoshua Bengio, et Aaron Courville.
  • Cours en Ligne :
    - Cours de deep learning par Andrew Ng sur Coursera.
    - Tutoriels et documentation officielle de PyTorch et Hugging Face Transformers.

Contacts

Academic staff

Registrar's office