Machine Learning

ECTS : 9

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

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