IA|BE is organizing for the fourth time a training program that will offer the possibility to obtain a certificate: the IA|BE Actuarial Data Scientist Certificate.
The objective of the training is to provide to the participants a deep understanding of key Data Science methods, their domain of applicability, key results of interest for practitioners and connections with the classical actuarial methods.
Moreover, the program emphasizes on applied programming practice and demystifies concepts such as model deployment in a corporate environment, MLOps, Object-Oriented programming, code management tools (e.g. Github), and principles of software engineering. For this purpose, Python has been chosen as programming language.
The program consists of 3 modules, each of which is followed by an assignment.
Each module consists of 4 theory sessions and 4 programming sessions.
The theory sessions will be given by the professors of the KU Leuven, UC Louvain and Université Libre de Bruxelles.
For the programming sessions, the Board of IA|BE has decided to cooperate with Milliman.
Participants who successfully complete the 3 modules will receive the Certificate of IA|BE Actuarial Data Scientist.
Each of these modules can also be taken separately.
Module 2: Machine Learning for Classification and Regressions
This module gradually introduces classification and regression trees up to competition winning ensemble methods for classification and regression problems.
Participants will explore how ensembles of decision trees achieve superior performance and learn how to calibrate them in practice. The module will also include one session on clustering, a common Data Science application.
More info about the third module is available here.
Price:
Members: 960 € / Module
Non-Members: 1.200 € / Module
Practical information:
The sessions will be set up online.
In order to guarantee optimal training conditions and interactivity of the programming sessions, the number of participants is currently limited to 25.
Schedule of Module 2: Machine Learning for Classification and Regressions
| Day 1, Thursday, 6 February | |
| 16:00 - 18:00 | Decision Trees in classification and regression (Part I) By TRUFIN Julien |
| Day 2, Thursday, 13 February | |
| 16:00 - 18:00 | Programming : Basics of regression and classification trees in Python By VAN DAM Daniel, VAN ES Raymond |
| Day 3, Thursday, 20 February | |
| 16:00 - 18:00 | Decision Trees in classification and regression (Part II) By TRUFIN Julien |
| Day 4, Thursday, 13 March | |
| 16:00 - 18:00 | Programming : From simple regression and classification trees to ensembles of trees (bagging and random forests) By VAN DAM Daniel, VAN ES Raymond |
| Day 5, Monday, 24 March | |
| 16:00 - 18:00 | Theory Boosted and Bagged ensembles By TRUFIN Julien |
| Day 6, Thursday, 27 March | |
| 16:00 - 18:00 | Programming: Stochastic gradient boosting machines and XGBoost By VAN DAM Daniel, VAN ES Raymond |
| Day 7, Monday, 31 March | |
| 16:00 - 18:00 | Neural Networks and deep learning overview By ANTONIO Katrien |
| Day 8, Thursday, 10 April | |
| 16:00 - 18:00 | Programming : Deep Learning By VAN DAM Daniel, VAN ES Raymond |
| Day 9, Wednesday, 30 April | |
| 18:00 - 23:59 | Assignment after Module 2 |