no code implementations • 30 Dec 2023 • Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, Sahan Bulathwela
With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future.
no code implementations • 20 Sep 2023 • Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, María Pérez-Ortiz, Sahan Bulathwela
This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems.
no code implementations • 10 Oct 2022 • Ben Dixon, María Pérez-Ortiz, Jacob Bieker
Solar PV yield nowcasting is used to help anticipate peaks and troughs in demand to support grid integration.
no code implementations • 8 Dec 2021 • Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas.
no code implementations • 3 Dec 2021 • Sahan Bulathwela, María Pérez-Ortiz, Catherine Holloway, John Shawe-Taylor
Artificial Intelligence (AI) in Education has been said to have the potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning.
1 code implementation • 7 Dec 2020 • Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz, John Shawe-Taylor
Many practical machine learning tasks can be framed as Structured prediction problems, where several output variables are predicted and considered interdependent.
1 code implementation • 25 Jul 2020 • María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári
In the context of probabilistic neural networks, the output of training is a probability distribution over network weights.
1 code implementation • 31 May 2020 • Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor
The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners.