Teaching plan for the course unit



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General information


Course unit name: Probabilistic Graphical Models

Course unit code: 572671

Academic year: 2019-2020

Coordinator: Jerónimo Hernández González

Department: Department of Mathematics and Computer Science

Credits: 3

Single program: S



Estimated learning time

Total number of hours 75


Face-to-face learning activities



-  Lecture




-  Lecture with practical component




-  Problem-solving class




-  IT-based class




-  Student presentation and discussion



Supervised project


Independent learning




Competences to be gained during study


CB6 ­ Poseer y comprender conocimientos que aporten una base u oportunidad de ser originales en el desarrollo y/o aplicación de ideas, a menudo en un contexto de investigación

CB9 ­ Que los estudiantes sepan comunicar sus conclusiones y los conocimientos y razones últimas que las sustentan a públicos especializados y no especializados de un modo claro y sin ambigüedades

CB10 ­ Que los estudiantes posean las habilidades de aprendizaje que les permitan continuar estudiando de un modo que habrá de ser en gran medida autodirigido o autónomo.

CE1 ­ Que los estudiantes sepan entender el proceso de valorización de los datos y su papel en la toma de decisiones.

CE2 ­ Que los estudiantes sepan recoger, extraer información y datos de fuentes de información estructuradas y no estructuradas.

CE7 ­ Que los estudiantes sepan entender, desarrollar y modificar los algoritmos analíticos y exploratorios que trabajan sobre conjuntos de datos y aplicar el pensamiento crítico en estas tareas.





Learning objectives


Referring to knowledge

To know what probabilistic graphical models (PGMs) are and what queries can we ask them.



To know when (and how) these queries can be answered exactly in polynomial time (exact inference).


To know what to do when they can not (approximate inference).


To know the basic techniques to learn probabilisitic graphical models from data.


Referring to abilities, skills

To be able to apply PGM algorithms to problems of your interest.



To be able to translate PGMs and related algorithms into code.



Teaching blocks






*  The objective of this first thematic block is to understand what a probabilisitic graphical model communicates in terms of statistical conditional independence assumptions. 



*  This second block deals with exact and approximate algorithms for answering probabilisitic queries to an already known PGM.  



*  This third block deals with how to learn PGM parameters and structure from data.


Modern applications

*  This fourth block overviews some state-of-the-art applications and tools in the area of PGMs.



Teaching methods and general organization


Lectures will be all encompassing iteratively including lecturing, problem solving and programming.

The students will be allowed to voluntarily provide a presentation on application of PGMs to problems of their interest or on a recently introduced PGM technique. 



Official assessment of learning outcomes


The subject will be evaluated based on a final exam (60%) and a presentation (40%).


Examination-based assessment

The subject will be evaluated based on a final exam (in the 0-10 scale).