Statistical foundations of machine learning

(1) Foundations of statistical modelling, (2) parametric estimation, (3) nonparametric estimation and resampling, (4) supervised learning ( model selection, variable selection), (5) algorithms for regression (neural networks, local learning, (6) classification algorithms (KNN, Naive- Bayes, SVM), (vii) applications of machine learning (data mining, text mining, web mining)

Statistical machine learning is the discipline which aims at extracting knowledge and inferring predictive models from observed data. The course will focus on the statistical notions (like bias, variance, parametric and nonparametric estimation, regression, validation) which are necessary to create, identify and assess a predictive model. This course aims to find a good balance between theory and practice by situating most of the theoretical notions in a real context with the help of illustrative case studies (from biology, finance, medicine) and real datasets.

Connaissances et compétences pré-requises ou co-requises

• Basic notions of probability and estimation (bias, variance)
• Linear algebra and numerical analysis (linear systems, eigenvalues)
• Least-squares
• Programming

Cours ayant celui-ci comme co-requis

5 ECTS (Th: 3, practicals: 1, project: 1)

Références, bibliographie et lectures recommandées

• Bontempi G., (2021) "Statistical foundations of machine learning: the handbook"

• T. Hastie, R. Tibshirani, J. Friedman (2002) The Elements of Statistical Learning. Springer.

Support(s) de cours

• Syllabus
• Université virtuelle

Informations complémentaires

All informations on UV page. 

Contacts

Email: Gianluca.Bontempi@ulb.be

Office: Campus La Plaine,

Postal address: Département d'Informatique, Bld de Triomphe, CP 212

Campus

Plaine

Méthode(s) d'évaluation

• Examen écrit
• Projet

Examen écrit

Projet

A project (in Python) and a written exam on the theoretical aspects of the course. The written exam (on the UV platform) will also require the use of the Python language to answer questions.

Construction de la note (en ce compris, la pondération des notes partielles)

• 10/20 (project in the first session)
• 10/20 (UV written exam on theory requiring the use of the Python software)

No project in the second exam session.
If the student is absent (ABS) for the 1st session exam, the project mark is no longer considered for the second session.

The professor reserves the right to invite any student to a supplementary oral interview to verify personal mastery of the exam content, particularly when inconsistencies in the examination do not allow the professor to correctly assess the level of mastery of the knowledge and skills covered by the teaching.

Langue(s) d'évaluation

• anglais
• (éventuellement français )