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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

> Studies > E3-STU-COURSES

- 5EU9MLO0

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  • Number of hours

    • Lectures : 26.0
    • Tutorials : -
    • Laboratory works : 24.0
    • Projects : 10.0
    • Internship : -
    ECTS : 5.0
  • Officials : Khaoula TIDRIRI

Goals

The objective of the course is to provide a comprehensive understanding of the theory and practice of Machine Learning and Optimization, mainly in the sphere of Smart Energy Systems.

At the end of the module, the students are 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 that are suitable for the applications under consideration.
  • Work with real-world datasets (analyze, understand, predict, prescribe) using data science and Artificial Intelligence tools.
  • Model an optimization problem with its all elements: decision variables, cost function, and constraints
  • Implement optimization algorithms that are suitable for the applications under consideration.

Content

A) Machine Learning Part(30h)

1) Introduction to Artificial Intelligence
2) Practical assessment – Python Ecosystem and Basics
3) Machine Learning Algorithms
3.1) Error based Machine Learning
3.2) Similarity based Machine Learning
3.3) Probability-based Machine Learning
3.4) Information-based Machine Learning
4) Practical assessment- Data preprocessing for ML
5) Practical assessment – Classification project
6) Practical assessment – Regression project
7) Machine Learning Project (10h)


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B) Optimization Methods Part (30h)

1) Optimization methods

2) Linear constrained optimization (Simplex)

3) Multi-objective optimization (Pareto)

Prerequisites

  • Python programming experience(Numpy, Pandas and Matplotlib)
  • Calculus, linear algebra, probabilities, and statistics.

Tests

« EVALUATION »

First session
ER assessment : Projects (Machine Learning part and Optimization part)
EN assessment : Exercices and Lab reports

If distant learning mandatory:
ER assessment : Project
EN assessment : Exercices and Lab reports

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Second session
EN assessment: Retaking this assessment is not possible

Course Unit assessment = 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 2020-2021

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

French State controlled diploma conferring a Master's degree

French State controlled diploma conferring a Master's degree
Université Grenoble Alpes