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