Panneau de gestion des cookies
NOTRE UTILISATION DES COOKIES
Des cookies sont utilisés sur notre site pour accéder à des informations stockées sur votre terminal. Nous utilisons des cookies techniques pour assurer le bon fonctionnement du site ainsi qu’avec notre partenaire des cookies fonctionnels de sécurité et partage d’information soumis à votre consentement pour les finalités décrites. Vous pouvez paramétrer le dépôt de ces cookies en cliquant sur le bouton « PARAMETRER » ci-dessous.

Machine Learning

Ects : 6

Enseignant responsable :

Volume horaire : 36

Description du contenu de l'enseignement :

The course gives a thorough presentation of the machine learning field and follows this outline:

  1. general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
  2. machine learning as automated program building from examples (running example: decision trees)
  3. machine learning as optimization:
    1. empirical risk minimization
    2. links with maximum likelihood estimation
    3. surrogate losses and extended machine learning settings
    4. regularisation and kernel methods (support vector machines)
  4. reliable estimation of performances:
    1. over fitting
    2. split samples
    3. resampling (leave-one-out, cross-validation and bootstrap)
    4. ROC curve, AUC and other advanced measures
  5. combining models:
    1. ensemble techniques
    2. bagging and random forests
    3. boosting
  6. unsupervised learning:
    1. clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)
    2. outlier and anomaly detection

Pré-requis obligatoires :

  • intermediate level in either Python or R. Students are expected to be able to perform standard data management tasks in Python or R, including, but not limited to:
    • loading a data set from a CSV file
    • recoding and cleaning the data set
    • implementing a simple data exploration strategy based on pivot table and on graphical representation
  • intermediate level in statistics and probability. Students are expected to be familiar with:
    • descriptive statistics
    • conditional probabilities and conditional expectations
    • core results from statistics: bias and variance concepts, strong law of large numbers, central limit theorem, etc.
Coefficient : 2 6 (M2 Economie Internationale et Développement) 6 (M2 Diagnostic économique international)

Compétence à acquérir :

After attending the course the students will

  • have a good understanding of the algorithmic and statistical foundations of the main machine learning techniques
  • be able to select machine learning techniques adapted to a particular task (exploratory analysis with clustering methods, predictive analysis, etc.)
  • be able to design a model selection procedure adapted to a particular task
  • report the results of a machine learning project with valid estimation of the performances of their model

Mode de contrôle des connaissances :

  • quizzes and tests during the course
  • machine learning project