Sacl-AI 4 Science Workshop

Session

Machine Learning for Science School

Jul 8, 2024, 9:30 AM

BÂTIMENT D’ENSEIGNEMENT MUTUALISÉ (BEM)

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)

26. Statistical learning and model validation

Olivier Colliot

7/8/24, 9:30 AM

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)

27. The scikit-learn API

Guillaume Lemaitre

7/8/24, 2:00 PM

Goal: Introduce the scikit-learn API, with a focus on practical insights on the model validation and selection.

- 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)

28. Learning with non-tabular data

Thomas Moreau (Inria)

7/9/24, 9:30 AM

Goal: Introduce the different types of data, with a focus on time-series, and the different methodologies to apply on each type.

- 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...

29. Introduction to deep learning

Romain Ménégaux

7/9/24, 2:00 PM

Goal: Describe the main types of deep learning architectures, and apply them to a concrete example from life sciences.

- 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...