Description
Goal: Introduce the basics of ML and describe in details how to perform validation
- History and terminology
- Problem setup for ML basics (Model, loss, learning procedure, features)
- Generalization in ML (overfitting, underfitting and model selection)
- Validation (performance metrics, validation strategies, statistical analysis)
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Olivier Colliot08/07/2024 09:30
Goal: Introduce the basics of ML and describe in details how to perform validation
- History and terminology
- Problem setup for ML basics (Model, loss, learning procedure, features)
- Generalization in ML (overfitting, underfitting and model selection)
- Validation (performance metrics, validation strategies, statistical analysis)
-
Guillaume Lemaitre08/07/2024 14:00
Goal: Introduce the scikit-learn API, with a focus on practical insights on the model validation and selection.
Aller à la page de la contribution- Overview of a simple cross-validation scheme k-fold - Overview of metrics (Regression, Classification) - Model selection through SearchCV - Cross validation in complex settings (stratification, groups, non-iid data) -
Thomas Moreau (Inria)09/07/2024 09:30
Goal: Introduce the different types of data, with a focus on time-series, and the different methodologies to apply on each type.
Aller à la page de la contribution- Overview of the different types of data: tabular data, time series, images, graph, signals. - Overview of the specific problems and jargon with time series and signals. - How to get back to a “classical” ML framework? - Practical illustrations... -
Romain Ménégaux09/07/2024 14:00
Goal: Describe the main types of deep learning architectures, and apply them to a concrete example from life sciences.
Aller à la page de la contribution- Introduction: what is deep learning and why is everyone doing it? - Overview of the main types of deep learning architectures: MLP, convolutional, and transformers. When to use one or the other? - Overview of the different training and regularization...