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tfg_champredon_axel.pdf (631.09 KB)

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Bachelor thesis

Publication date

2024-06-09

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cc-by-nc-nd (c) Axel Champredon Le Mercier, 2023
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/218121

Learning theory and out of distribution detection

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Abstract

Nowadays, Machines are starting to have a really important relevance in automation tasks. Learning could be considered as one of the hardest tasks we can encounter. The goal of this work is to introduce the theoretical foundations of this topic. After a short introduction on general learning and machine learning in chapter 1, we will introduce the fundamental concepts of Learning Theory in chapter 2, we will find what learning formally means and under which conditions a scenario can be learnable. In the last chapter we will introduce a pretty recent topic: Out of Distribution detection. A theory that appeared for the first time in 2017 and tries to formalize whether or not it would be possible for a machine to detect if we are trying to make predictions on data which it hasn’t been trained for. Again, we will try to find conditions under which a machine could learn this skill.

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Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2024, Director: Josep Vives i Santa Eulàlia i Santi Seguí Mesquida

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Aprenentatge automàtic, Sistemes classificadors (Intel·ligència artificial), Probabilitats, Teoria de la mesura, Treballs de fi de grau

Subject (English)

Machine learning, Learning classifier systems, Probabilities, Measure theory, Bachelor's theses

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Treballs Finals de Grau (TFG) - Matemàtiques

Citation

CHAMPREDON LE MERCIER, Axel. Learning theory and out of distribution detection. [consulted: 13 of August of 2026]. Available at: https://hdl.handle.net/2445/218121

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