Teacher(s)
Language
English
Prerequisites
- This course is reserved for students with a bachelor’s degree in business engineering (“ingénieur de gestion”), who have the proper background in probability, statistics, econometrics and finance.
Students with equivalent quantitative method skills who wish to enroll in this course should refer to the following courses as the assumed prerequisites: - LINGE1113 Probabilités
- LINGE1114 Mathématiques: analyse
- LINGE1121 Mathématiques: algèbre et calcul matriciel
- LINGE1214 Statistiques approfondies
- LINGE1222 Analyse statistique multivariée (very relevant)
- LINGE1221 Econométrie
- LINGE1315 Finance
Main themes
The course will cover important developments in the fields of statistical learning, machine learning and big data. These interrelated fields provide statistical models to learn structure from high-dimensional data and make accurate predictions.
The course is divided in four sections:
The course is divided in four sections:
- Robust linear regression
- Principal and independent component analysis
- Bayesian estimation
- Ensemble learning
Learning outcomes
At the end of this learning unit, the student is able to : | |
1 | By the end of the course, the student will have mainly developed the following elements of the « référenciel de compétence » of the Louvain School of Management. From the lectures: 2. Maîtriser des savoirs, 3. Appliquer une démarche scientifique. From the group assignment : 6. Travailler en équipe et en exercer le leadership, 7. Gérer un projet, 8. Communiquer. |
Content
This course covers theoretical and practical concepts related to:
- Robust linear regression:
- Principal and independent component analysis:
- Bayesian estimation:
Teaching methods
Lectures + group assignment
Evaluation methods
Final exam in session + group assignment
Faculty or entity