CIMPA School 2027
From Statistical Inference to Learning Methods (SILM)
March 15 - 26, 2027 | ENSGMM/UNSTIM - Abomey (Benin)
Courses
Introductory Courses
Probability and Statistical Foundations for Learning Methods
This course revisits fundamental concepts of probability and statistics with a focus on methods and tools essential for statistical learning. Participants will strengthen their understanding of probabilistic modeling, stochastic dependencies, and inferential techniques in multivariate and high-dimensional contexts. Applications in diverse domains such as health, engineering, social sciences, and general data-driven decision-making will illustrate theoretical concepts. This course bridges solid statistical foundations with modern statistical learning methods, preparing participants for advanced courses on classification, regression, and machine learning.
Instructor: Freedath DJIBRIL MOUSSA
Sessions: 4 × 1h30 | Total: 6h00
Prediction and Classification Methods
This course introduces participants to key statistical and algorithmic methods for prediction and classification, bridging traditional statistical inference and modern learning techniques. Topics include supervised learning, model evaluation, and classification algorithms such as logistic regression, k-nearest neighbors, and decision trees. Emphasis is placed on practical applications in finance and other domains where predictive modeling is essential. Participants will learn to implement these methods in R or Python, interpret model outputs, and evaluate performance using cross-validation and error metrics. This course prepares participants for more advanced topics in statistical learning explored later in the school.
Instructor: Mintodê Nicodème ATCHADE
Sessions: 4 × 1h30 | Total: 6h00
Tools and Methods in Statistical Learning
This course provides a robust introduction to statistical learning methods, bridging classical statistical techniques with modern machine learning approaches. Participants will explore supervised learning, unsupervised learning, and model evaluation methods, focusing on applications in finance, insurance, and other domains where predictive modeling is essential. Emphasis is placed on understanding algorithms from a statistical perspective, ensuring participants gain both theoretical and practical insights. Hands-on exercises using R and Python will help consolidate the concepts.
Instructor: Benoît LIQUET
Sessions: 4 × 1h30 | Total: 6h00
Total introductory: 18h00
Advanced Courses
Machine Learning for Censored Data
Censored and truncated data frequently arise in survival analysis, reliability studies, and financial risk modeling. Traditional statistical methods (e.g., Kaplan-Meier estimator, Cox proportional hazards model) provide strong tools, but recent advances in machine learning have opened new avenues for handling high-dimensional covariates, nonlinear effects, and complex censoring mechanisms. This course introduces modern machine learning approaches for censored data, combining theoretical insights with practical implementations. Topics include extensions of regression models to censored data, survival trees and forests, boosting methods, and neural network-based survival models. Applications will be drawn from credit risk, insurance, and biomedical studies.
Instructor: Christian PAROISSIN
Sessions: 4 × 1h30 | Total: 6h00
Machine Learning for Financial Mathematics
This course explores the integration of machine learning methods into financial mathematics, focusing on how advanced statistical learning techniques can be applied to problems in pricing, risk management, portfolio optimization, and financial forecasting. Participants will learn to bridge classical stochastic and mathematical finance with modern machine learning algorithms. The course will emphasize both theoretical underpinnings and practical applications through real financial data.
Instructor: Gero JUNIKE
Sessions: 4 × 1h30 | Total: 6h00
Making Decisions under Uncertainty
This course introduces stochastic control and sequential decision-making. The focus is on modifying the natural trajectory of a stochastic process to optimize an objective function. The course covers Markov Decision Processes (MDPs), dynamic programming, solution algorithms, and extensions to partially observed problems or reinforcement learning for unknown models.
Instructor: Benoite DE SAPORTA
Sessions: 4 × 1h30 | Total: 6h00
Machine Learning and Extreme Value Data
This course introduces methods for analyzing and modeling rare and extreme events using Extreme Value Theory (EVT) combined with machine learning techniques. Topics include block maxima, peaks-over-threshold methods, tail index estimation, and dependence modeling. Applications will be illustrated in finance, insurance, and environmental sciences. Hands-on sessions will cover the implementation of EVT estimators and machine learning algorithms (R/Python).
Instructor: El-Hadj DEME
Sessions: 4 × 1h30 | Total: 6h00
Total advanced: 24h00
Exercise Sessions
Probability and Statistical Foundations for Learning Methods
Hands-on exercises and applications using R/Python: simulation of multivariate distributions, estimation of parameters, model fitting and validation, simple predictive modeling tasks on real-life datasets.
Instructor: Freedath DJIBRIL MOUSSA
Sessions: 1 × 1h30 | Total: 1h30
Prediction and Classification Methods
Hands-on exercises on predictive and classification modeling and applications using R/Python, applying methods to financial datasets or simulated examples.
Instructor: Mintodê Nicodème ATCHADE
Sessions: 1 × 1h30 | Total: 1h30
Tools and Methods in Statistical Learning
Hands-on exercises and implementation of regression, classification, and tree-based methods in R or Python; cross-validation and model evaluation; application to real-world datasets; basic introduction to neural network modeling.
Instructor: Benoît LIQUET
Sessions: 1 × 1h30 | Total: 1h30
Total exercise sessions: 4h30
Total duration of courses: 46h30
