La décomposition par substitution des permutations permet de voir ces objets combinatoires comme des arbres. Je présenterai d'abord cette décomposition par substitution, et les arbres sous-jacents, appelés arbres de décomposition. Puis j'exposerai une méthode, complètement algorithmique et reposant sur les arbres de décomposition, qui permet de calculer des spécifications combinatoires de classes de permutations à motifs interdits. La connaissance de telles spécifications combinatoires ouvre de nouvelles perspectives pour l'étude des classes de permutations, que je présenterai en conclusion.[-]

This course introduces fundamental concepts in machine learning and presents some classical approaches and algorithms. The scikit-learn library is presented during the practical sessions. The course aims at providing fundamental basics for using machine learning techniques.
Class (4h)
General Introduction to Machine Learning (learning settings, curse of dimensionality, overfitting/underfitting, etc.)
Overview of Supervised Learning: True risk/Empirical risk, loss functions, regularization, sparsity, norms, bias/variance trade-off, PAC generalization bounds, model selection.
Classical machine learning models: Support Vector Machines, Kernel Methods, Decision trees and Random Forests.
An introduction to uncertainty in ML: Gaussian Processes, Quantile Regression with RF
Labs (4h)
Introduction to scikit-learn
Classical Machine learning Models with scikit-learn
Uncertainty in ML[-]

This course introduces fundamental concepts in machine learning and presents some classical approaches and algorithms. The scikit-learn library is presented during the practical sessions. The course aims at providing fundamental basics for using machine learning techniques.
Class (4h)
General Introduction to Machine Learning (learning settings, curse of dimensionality, overfitting/underfitting, etc.)
Overview of Supervised Learning: True risk/Empirical risk, loss functions, regularization, sparsity, norms, bias/variance trade-off, PAC generalization bounds, model selection.
Classical machine learning models: Support Vector Machines, Kernel Methods, Decision trees and Random Forests
An introduction to uncertainty in ML: Gaussian Processes, Quantile Regression with RF
Labs (4h)
Introduction to scikit-learn
Classical Machine learning Models with scikit-learn
Uncertainty in ML[-]

This course introduces fundamental concepts in machine learning and presents some classical approaches and algorithms. The scikit-learn library is presented during the practical sessions. The course aims at providing fundamental basics for using machine learning techniques.
Class (4h)
General Introduction to Machine Learning (learning settings, curse of dimensionality, overfitting/underfitting, etc.)
Overview of Supervised Learning: True risk/Empirical risk, loss functions, regularization, sparsity, norms, bias/variance trade-off, PAC generalization bounds, model selection.
Classical machine learning models: Support Vector Machines, Kernel Methods, Decision trees and Random Forests.
An introduction to uncertainty in ML: Gaussian Processes, Quantile Regression with RF
Labs (4h)
Introduction to scikit-learn
Classical Machine learning Models with scikit-learn
Uncertainty in ML[-]

This course introduces fundamental concepts in machine learning and presents some classical approaches and algorithms. The scikit-learn library is presented during the practical sessions. The course aims at providing fundamental basics for using machine learning techniques.
Class (4h)
General Introduction to Machine Learning (learning settings, curse of dimensionality, overfitting/underfitting, etc.)
Overview of Supervised Learning: True risk/Empirical risk, loss functions, regularization, sparsity, norms, bias/variance trade-off, PAC generalization bounds, model selection.
Classical machine learning models: Support Vector Machines, Kernel Methods, Decision trees and Random Forests
An introduction to uncertainty in ML: Gaussian Processes, Quantile Regression with RF
Labs (4h)
Introduction to scikit-learn
Classical Machine learning Models with scikit-learn
Uncertainty in ML[-]