| Additional title: |
Advanced Machine Learning |
| Additional title: |
Aprendizaje Automático Avanzado |
| Date: |
2024-25 |
| Abstract: |
This subject aims to give a detailed overview of techniques that enhance and optimise neural network models beyond the implementation of task-specific architectures. Building upon the foundation of previous subjects, this course focuses on meta-objectives that transfer, extend, or analyse the operation of existing models. Students will push their existing understanding of the mechanics of deep learning and address a range of practical challenges in deploying and scaling neural network models in realistic settings. They will learn to train models in resource-constrained settings; probe the behaviour of models to understand their failure cases; learn to subvert models as an adversary, and design models that are secure against subversion. By the end of this subject, students should have developed skills and expertise to think about secondary objectives in the development of deep models, and be able to reason about the behaviour of neural networks at a high level. They will also have practical experience with these advanced deep learning techniques, equipping them with the knowledge to create systems that are more adaptable, efficient, and robust. |
| Rights: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.  |
| Language: |
Català, anglès, castellà |
| Studies: |
Grau en Intel·ligència Artificial [2504392] |
| Study plan: |
Intel·ligència Artificial [1497] |
| Document: |
Objecte d'aprenentatge |
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