Unité pédagogique
AI Basics - Machine Learning
Derniere édition le: 17/06/2024
Modifier
Responsable:
DALMAS Benjamin
Description générale :
This course is given in English as it corresponds to part of a Teaching Unit of the International MSc on Cyber Physical and Social Systems (CPS2): AI and IoT
The module gives a theoretical overview of the various forms of machine learning. First, we have a glance at unsupervised techniques that help us understand the data. Then, we focus on various supervised learning systems used in practice such as linear models, non linear models (in particular, neural networks), non parametric models, and support vector machines. We show how ensembles of models can outperform a single model, and we detail the particular case of deep neural networks and deep learning. Finally, we study how agents can learn what to do in the absence of labeled examples of what to do. We see how agents can learn from past experience to change their behavior using reinforcement learning techniques (Q-learning). Labs will be performed using python libraries like scikit-learn, pandas or numpy.
Mots-clés:
Machine learning
neural networks
Apprentissage par reforcement
Nombre d’heures à l’emploi du temps:
30
Domaine(s) ou champs disciplinaires:
Langue d’enseignement:
Anglais
Objectifs d’apprentissage:
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Examen sur table :
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Exercice pratique collectif :
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Exercice pratique individuel :
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Rapport collectif :
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Programme et contenus:
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Contenu, séquencement et organisation |
| Supervised work |
(15h) |
| Labs |
(7h30) |