Ph.D. Student: Rafael Barbudo Lunar
Advisors: José Raúl Romero, Sebastián Ventura
Started on: October 2018
Keywords: automated machine learning, democratisation


Extracting useful and novel knowledge from raw data is a complex process, which apart from technical expertise, requires a considerable amount of background in the application domain. Therefore, it is desirable to bring the phases composing such a process closer to the domain experts. In this vein, the field known as Automated Machine Learning (AutoML) has emerged to precisely automate the machine learning process, which plays a key role during the extraction of valuable knowledge. Automating those repetitive and time-consuming phases allows technical experts to shift the focus to those phases requiring from their know-how and intuition. Indeed, AutoML approaches have already shown that they can outperform technical experts in certain tasks like the design of neural network architectures.

The main objective of this thesis is to develop AutoML approaches that assist both data scientist and end users during the knowledge extraction process.

The partial objectives are the following:

  • Developing an AutoML tool, which automates the generation of an optimised machine learning workflow.
  • Reducing the gap between machine learning techniques and the experts on the application domain, thus making the data science process more accessible to end users.


The development of this thesis is being supported by:

  • Spanish Ministry of Science and Competitiveness, project TIN2017-83445-P.
  • Spanish Ministry of Education, Culture and Sports under the FPU program (FPU17/00799).


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