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:

  • general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)

  • machine learning as automated program building from examples (running example: decision trees)

  • machine learning as optimization:
    empirical risk minimizationlinks with maximum likelihood estimationsurrogate losses and extended machine learning settingsregularisation and kernel methods (support vector machines)

  • reliable estimation of performances:
    over fittingsplit samplesresampling (leave-one-out, cross-validation and bootstrap)ROC curve, AUC and other advanced measures

  • combining models:
    ensemble techniquesbagging and random forestsboosting

  • unsupervised learning:
    clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)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 filerecoding 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 statisticsconditional probabilities and conditional expectationscore results from statistics: bias and variance concepts, strong law of large numbers, central limit theorem, etc.

    Coefficient : 6
    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