セクションアウトライン
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Dates: 18/11 (14h15 - 18h30), Vu-Linh Nguyen
This first lecture dedicated to uncertainty in machine learning will provide some first illustration as to how the mathematical elements of the previous lectures can be used in machine learning. This will notably be done through simple illustrations and examples.
Objectives of the lecture:
After the lectures, the students should be able to
- Understand the basics of the Imprecise Dirichlet Model (IDM)
- Apply it to a simple local learning scheme
- Implement decision rules to this specific learning scheme
- Identify the main sources of uncertainty
- Have a basic understanding of the challenges underlying the evaluation of cautious classifiers