Schedule of Actuarial Data Scientist Program: Fifth edition - Module 1 (16 CPD)
| Day 1, Thursday, 12 November | |
| 16:30 - 18:30 | Linear Models and conditional estimation By ANTONIO Katrien |
| Day 2, Monday, 16 November | |
| 16:30 - 18:30 | Programming : Foundations of actuarial learning and the organization of the training By BLOM Bjorn |
| Day 3, Monday, 23 November | |
| 16:30 - 18:30 | Generalized Linear Models By ANTONIO Katrien |
| Day 4, Tuesday, 24 November | |
| 16:30 - 18:30 | Programming : LMs and GLMs By BLOM Bjorn |
| Day 5, Thursday, 26 November | |
| 16:30 - 18:30 | Regularisations and links with other support vector machines By ANTONIO Katrien |
| Day 6, Monday, 30 November | |
| 16:30 - 18:30 | Programming: Regularisations By BLOM Bjorn |
| Day 7, Wednesday, 2 December | |
| 16:30 - 18:30 | Clustering methods By HAINAUT Donatien |
| Day 8, Monday, 7 December | |
| 16:30 - 18:30 | Programming: Clustering By BLOM Bjorn |
| Day 9, Sunday, 24 January | |
| 18:00 - 23:59 | Assignment after Module 1 |
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- Conditional mean estimation E[Y|X] and the iris problem.
- Introduction to Classification problems in machine learning: Linear Discriminant Analysis.
- Introduction to Regression Problem: Linear models and the OLS estimator (with mixed data types: e.g. mix of continuous and discrete data).
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From 16:30 to 18:30
Programming : Foundations of actuarial learning and the organization of the training
By BLOM Bjorn- Introducing the trainers and the training environment, including a first introduction to git and the GitHub repo’s dedicated to the training, the notebooks, data sets available, the ways to execute the Python code
- Introducing the data sets that will be analyzed in the course: MTPL claim frequency and severity data, Ames Housing data set, caravan insurance data set (for a classification problem with class imbalance), data set with characteristics of vehicles (for clustering).
- Getting to know these data sets: basic data explorations, some plotting, data manipulation and calculating summary statistics [Numpy, pandas and Matplotlib].
- Target and feature engineering steps, including (among others) centering, scaling, dealing with NAs, class imbalance, filter out near zero variance [scikit-learn: sklearn.preprocessing].
- Data splitting and resampling methods: training vs validation vs test, k-fold cross validation [scikit-learn: sklearn.model_selection].
- Introduction to parameter tuning, simple example with e.g. K-nearest-neighbour. [scikit-learn: sklearn.model_selection].
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- the GLMs (Logistic, Poisson, Gamma)
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- Introducing the Python package statsmodels via [statsmodels.api] and the support for formulas via [statsmodels.formula.api].
- Linear regression: describe the models, fit and summarize linear regression models on the Ames Housing data: model fit and model inspection, prediction, variable and model selection tools [statsmodels: statsmodels.regression.linear_model]
- Generalized linear regression models: fitting Poisson and gamma regression models on the MTPL data set: inspecting model fit, building predictions, evaluating model fit [statsmodels: statsmodels.genmod.generalized_linear_model].
- We gradually build up the Poisson GLM: introducing exposure (offset), how to handle multiple types of variables (numeric, categorical).
- Combine frequency and severity GLMs into a technical tariff. Construct technical prices for selected risk profiles.
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- Introduces regularization as a way to control model complexity and prevent overfitting
- Covers LASSO, Ridge, and ElasticNet, and how each shapes model coefficients differently
- Draws the connection to Support Vector Machines and their shared foundation in balancing fit against complexity
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- Implement and compare LASSO, Ridge, and ElasticNet in Python on real data
- Tune the regularization strength and observe its effect on coefficients and performance
- Experiment with Support Vector Machines to see the shared principles at work
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- Introduces unsupervised learning: finding structure in data without predefined labels
- Covers common clustering techniques for grouping similar policyholders, risks, or observations
- Shows how clustering supports segmentation, a common tool in actuarial practice
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- Apply clustering techniques in Python to real data
- Group similar policyholders, risks, or observations and interpret the resulting segments
- Experiment with the number of clusters and compare how different choices affect the results
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From 18:00 to 23:59
Assignment after Module 1
Important: The assignment is not a strict examination — its purpose is to apply the concepts learned during the sessions. Participants who selected the Feedback ticket will receive detailed, personalized feedback on their submission.
Deadline for handing in the assignment: 24 January 2027