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Machine Learning

El Aprendizaje Automático trata de construir sistemas informáticos que optimicen un criterio de rendimiento utilizando datos o experiencia previa.

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concha BIELZA


Departamento de Inteligencia Artificial.
Facultad de Informática.

Optativa, máster
Máster Universitario en Inteligencia Artificial por la UPM

Last review (Noviembre 2012).


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Créditos ECTS: 5.



Machine Learning deals with  building computer systems that optimise performance criteria using previous data or experience. A situation where learning is required is when there is no human experience or it is not easily explained. Another is when the problem to be solved changes over time or depends on a particular environment.

Machine Learning transforms data into knowledge and provides general purpose systems that adapt to circumstances. Among the many successful applications that can be cited are: speech recognition or handwritten text, autonomous robot navigation, document information retrieval, cooperative filtering, diagnostic systems, DNA microarrays analysis, etc.

This module presents several methods based on different fields such as Statistics, Pattern Recognition, Artificial Intelligence and Data Mining. The aim is to know these methods from a unified perspective, noting which problems can be solved, as well as the limitations and circumstances of using each one of them.



- To be able to model real problems of classification by means of computing paradigms.

- To be able to recognise where the boundaries of knowledge are in machine learning by means of critical reading of relevant scientific publications, usually written in English.

- To be able to contribute with new ideas, both at a methodological level as well as in the application of learning machine, going beyond the frontiers of knowledge.

- To be able to express the ideas of the state of the art and newly contributed ideas, both orally and writing.




- To deliver two individual oral presentations about the state of the art based on module's content; one of them about supervised classification and the other one about unsupervised classification. Clarity when presenting and scope of the state of the art covered will be valued. This weighs 60% in the final grade. 

- To write an individual assignment, proposed by the student or professor, which allows to go more deeply into some topic of the subject. Innovative aspects of the work will be particularly valued. The weight of this part is 40%. 

Assessment for the extraordinary july examination will be similar (two oral presentations and an individual written assignment).



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Copyright 2009, Autores y colaboradores. Reconocer autoría/Citar obra. Bielza, C., Larrañaga, P. (2010, March 08). Machine Learning. Retrieved March 21, 2019, from OCW UPM - OpenCourseWare de la Universidad Politécnica de Madrid Web site: http://ocw.upm.es/ciencia-de-la-computacion-e-inteligencia-artificial/machine-learning. Esta obra se publica bajo una licencia Licencia Creative Commons Licencia Creative Commons