Probabilistic Graphical Models

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Credits
4.5
Department
UB
Types
Elective
Requirements
This subject has not requirements
Probabilistic Graphical Models are a core technology for machine learning, decision making, machine vision, natural language processing and many other artificial intelligence applications. In this course we provide an overview of the subject. We review the formal theoretical foundations and we perform a practical project where the student can apply the technology successfully to a problem of his interest.

Teachers

Person in charge

  • Jesus Cerquides ( )

Weekly hours

Theory
1
Problems
0.7
Laboratory
0.5
Guided learning
0
Autonomous learning
4

Competences

Transversal Competences

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

Solvent use of the information resources

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Reasoning

  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

Analisis y sintesis

  • CT7 - Capability to analyze and solve complex technical problems.

Technical Competences of each Specialization

Academic

  • CEA12 - Capability to understand the advanced techniques of Knowledge Engineering, Machine Learning and Decision Support Systems, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA13 - Capability to understand advanced techniques of Modeling , Reasoning and Problem Solving, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA3 - Capability to understand the basic operation principles of Machine Learning main techniques, and to know how to use on the environment of an intelligent system or service.
  • CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.

Professional

  • CEP1 - Capability to solve the analysis of information needs from different organizations, identifying the uncertainty and variability sources.
  • CEP2 - Capability to solve the decision making problems from different organizations, integrating intelligent tools.
  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
  • CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.

Generic Technical Competences

Generic

  • CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.

Objectives

  1. Be able to use effectively Probabilistic Graphical Models in business and research scenarios.
    Related competences: CB6, CB9, CT4, CT6, CT7, CEA12, CEA13, CEA3, CEA8, CEP1, CEP2, CEP3, CEP5, CG3,

Activities

Lecture

Collaborative style lectures
Theory
0
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0
Objectives: 1

Teaching methodology

Lectures will be given where the main contents will be presented to the students. The students will be requested to do exercises to increase and deepen the knowledge acquired in the lectures. Finally an exam will validate the learning outcome of the students.

Evaluation methodology

The course will be marked by an examination and through the evaluation of the exercises requested to the students along the course. However, there is room for a different evaluation method to be agreed between the students and the course professor when the course begins.

Web links

Previous capacities

The course requires the student to have some minor knowledge of linear algebra and some concepts of calculus. He should be proficient in algorithmics as well.