Unité pédagogique
Derniere édition le: 26/09/2024
ModifierMachine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. The objectives of this course is to give the students the basic knowledges of the main tools of machine learning, in order to be able to have a technical discussion with experts of the domain. Tutorials will be performed with the Python language on real data, coming from industries of the field of process engineering.
The notions that will be discussed are : ACP/SVD, Linear regression and logistic regression, Supervised and unsupervised classification, Continuous optimization, Decision trees, SVM, Random forests, Neural networks.
Application : a real and huge dataset will be provided in order to optimize a complete reactor process.
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| Type d’activité pédagogique : | Contenu, séquencement et organisation |
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