Teaching plan for the course unit

 

 

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General information

 

Course unit name: Probabilistic Graphical Models

Course unit code: 572671

Academic year: 2021-2022

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 and/or online activities

30

 

-  Lecture with practical component

Face-to-face

 

14

 

-  Problem-solving class

Face-to-face

 

7

 

-  IT-based class

Face-to-face

 

7

 

-  Student presentation and discussion

Face-to-face

 

2

Supervised project

15

Independent learning

30

 

 

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 we can ask them.

 

 

To know when (and how) queries can be answered exactly in polynomial time (exact inference), and what to do when they can not (approximate inference).

 

To know the basic techniques to learn probabilisitic graphical models from data, even when data is partially missing.

 

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

 

No..

Title

1

Representation

2

Inference

3

Learning from data

4

Modern trends, applications and tools

 

 

Teaching methods and general organization

 

Lectures dynamically combine master explanations and problem solving. Some slots may be exclusively dedicated to programming throughout directed activities or notebooks.

The students will be required to make a presentation regarding an application (their own or other people’s) of PGMs to a problem of their interest, or a recently proposed study about PGMs.

 

 

Official assessment of learning outcomes

 

The subject is expected to be evaluated based on a final exam (40%), a presentation (30%) and in-class activities (30%).

 

Examination-based assessment

The subject is expected to be evaluated based on a final exam (50%) and a project (50%).