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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:
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« 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 
The course exists in the following branches:
))))))))Course ID : 5EU9MLO0
Course language(s):
You can find this course among all other courses.
Date of update September 3, 2020
