Machine learning is an increasingly important area, and it has provided
many of the recent advances behind applications of artificial intelligence.
It is relevant to a plethora of application domains in science and industry
- including in finance, health, transport, linguistics, media, and biology.
Lectures will cover the many of the main concepts and algorithms. We will
cover in some degree all the main paradigms of machine learning: supervised
learning (regression, classification), unsupervised learning, and
reinforcement learning. Among many learning algorithms we will look at:
least squares, logistic regression, k-nearest neighbors, neural networks
and deep learning, decision tree inducers, kernel methods, PCA, k-means
clustering, and Q-learning. In the labs, we will implement many of these,
and investigate their use in different applications. Programming will be
done in Python with scientific libraries such as numpy and scikit-learn.
The main grading component is a team project, as well as several in-class
Format des notes
Validé / non validé
Pour les étudiants du diplôme Bachelor of Science de l'Ecole polytechnique
Le rattrapage est autorisé (Note de rattrapage conservée)L'UE est acquise si Note finale >=
- Crédits ECTS acquis : 5 ECTS
Pour les étudiants du diplôme Echanges PEI
Le rattrapage est autorisé (Note de rattrapage conservée)