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Grenoble INP - UGA
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Ecole nationale supérieure de l'Énergie, l'Eau et l'Environnement
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Engineering school in energy, water and environment
Our engineering & master degrees
Our engineering & master degrees

> Study at Ense3 > Double degrees > E3-STU-COURSES

Machine Learning and Optimization - 5EU9MLO0

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  • Number of hours

    • Lectures : -
    • Tutorials : -
    • Laboratory works : 50.0
    • Projects : -
    • Internship : -
    • Written tests : -
    ECTS : 5.0
  • Officials : Vincent DEBUSSCHERE

Goals

The objective of the course is to provide a comprehensive understanding of the theory and practice of machine learning, primarily in the area of smart energy systems.

At the end of the module, students will be able to:

  • Understand the fundamentals of machine learning algorithms (error-based, similarity-based, probability-based and information-based learning algorithms.)
  • Select and implement machine learning methods appropriate to the applications under consideration.
  • Work with real-world data sets (analyze, understand, predict, prescribe) using data science and artificial intelligence tools.

Content

Lab part

1) Introduction to machine learning
2) Supervised learning (classification, regression)
3) Unsupervised Learning (Clustering)
4) Data Analysis (Pandas-Python)
5) Project: Estimation occupancy & Load forecasting

Practical work part

1) Discovery of database and visualization of data (InfluxDB, Grafana)
2) Analysis of data provided by a real home automation (ExpeSmartHouse)

Prerequisites

  • Experience in Python programming (Numpy, Pandas and Matplotlib)

Tests

  • Specific credits: this course brings 3.0 ECTS to students in Year 2 Master Electrical Engineering for Smart Grids and Buildings (M2 SGB)
  • Regular session
    Evaluation with possible retake (ER): ML project
    Evaluation without retake (EN): Exercises and lab reports (ML and Optimization)

If 100% distance learning
Evaluation with possible retake (ER): Projects
Evaluation without retake (EN): Exercises and lab reports

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Remedial session
EN : Evaluation without retake

Average of the UE = ER 60% + EN 40%

The exam is given in english only FR

Calendar

The course exists in the following branches:

see the course schedule for 2022-2023

Additional Information

Course ID : 5EU9MLO0
Course language(s): FR

You can find this course among all other courses.

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Date of update September 3, 2020

Université Grenoble Alpes