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Cours scientifique - MAP654I : Practical introduction to machine learning

Domaine > Mathématiques appliquées.

Descriptif

The objective of this course is to provide a practical introduction to the field of machine learning. We will discuss the different machine learning problems from unsupervised (dimensionality reduction, clustering and density estimation) to supervised (classification, regression, ranking). In this course we will introduce for each method the problem, provide its modeling as an optimization problem and discuss the algorithms that are used to solve the problem. The practical aspect of each method will also be discussed along with python code and existing implementations.

The course will be completed by practical sessions that will allow the students to implement the methods seen in the course on practical problems such as image classification and time series prediction (biomedical and climate data). The objective of the practical session will be not only to learn to use the methods but also to interpret their models and results with respect to the data and the theoretical models.

Course overview:

  • Introduction
    • Machine learning problems
    • Knowing your data
    • Preprocessing
  • Unsupervised learning
    • Dimensionality reduction and
    • Dictionary learning and collaborative filtering
    • Clustering and generative modeling
    • Generative modeling
  • Supervised learning
    • Linear models and kernel methods for regression and classification
    • Nearest neighbors and bayesian decision
    • Trees and ensemble methods
  • ML in practice
    • Find your problem
    • Model selection

This course will be given in english with lecture material in english.

Evaluation : practical session reports and oral








The objective of this course is to provide a practical introduction to the field of machine learning. We will discuss the different machine learning problems from unsupervised (dimensionality reduction, clustering and density estimation) to supervised (classification, regression, ranking). In this course we will introduce for each method the problem, provide its modeling as an optimization problem and discuss the algorithms that are used to solve the problem. The practical aspect of each method will also be discussed along with python code and existing implementations.

The course will be completed by practical sessions that will allow the students to implement the methods seen in the course on practical problems such as image classification and time series prediction (biomedical and climate data). The objective of the practical session will be not only to learn to use the methods but also to interpret their models and results with respect to the data and the theoretical models.

Course overview:

  • Introduction
    • Machine learning problems
    • Knowing your data
    • Preprocessing
  • Unsupervised learning
    • Dimensionality reduction and
    • Dictionary learning and collaborative filtering
    • Clustering and generative modeling
    • Generative modeling
  • Supervised learning
    • Linear models and kernel methods for regression and classification
    • Nearest neighbors and bayesian decision
    • Trees and ensemble methods
  • ML in practice
    • Find your problem
    • Model selection

This course will be given in english with lecture material in english.

Evaluation : practical session reports and oral














effectifs minimal / maximal:

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Diplôme(s) concerné(s)

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Numérique sur 20

Littérale/grade réduit

Pour les étudiants du diplôme Data Sciences

Le rattrapage est autorisé (Max entre les deux notes)
    L'UE est acquise si Note finale >= 10
    • Crédits ECTS acquis : 3 ECTS
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