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 tests.
Format des notesNumérique sur 20Littérale/grade américain
Pour les étudiants du diplôme Echanges PEILe rattrapage est autorisé
Pour les étudiants du diplôme Bachelor of Science de l'Ecole polytechniqueLe rattrapage est autorisé (Max entre les deux notes écrêté à une note seuil)
- Crédits ECTS acquis : 4 ECTS
La note obtenue rentre dans le calcul de votre GPA.