Machine learning

Ects : 6
Volume horaire : 36
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
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.

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

Enseignant responsable :

  • FABRICE ROSSI