Probabilistic Graphical Models

You are here

Home » Studies » Masters » Master in Artificial Intelligence » Curriculum » Syllabus » Probabilistic Graphical Models
Credits
4.5
Types
Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
UB;CS
Mail
Probabilistic Graphical Models are a core technology for machine learning, decision making, computer vision, natural language processing and many other artificial intelligence applications. In this subject, we provide an overview of the topic. We review the formal theoretical foundations and we study how to put them in practice in order to solve problems of your interest.

Weekly hours

Theory
1
Problems
1
Laboratory
0.5
Guided learning
0
Autonomous learning
5

Competences

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.

Technical Competences of each Specialization

Academic

  • 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.
  • 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.

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.

Transversal Competences

Information literacy

  • 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.

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.

Objectives

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

Contents

  1. Representation
    Formal description of PGMs and different types
  2. Inference
    Using PGMs to answer probabilistic queries (both exactly and approximately)
  3. Learning
    Learning PGMs from data (both parameters and graph structure)
  4. Modern trends, applications and tools
    PGMs state-of-the-art

Activities

Activity Evaluation act


Development of the first subject's block: Representation

Collaborative style lectures
Objectives: 1
Contents:
Theory
4h
Problems
4h
Laboratory
2h
Guided learning
0h
Autonomous learning
15h

Development of the second subject's block: Inference



Contents:
Theory
4h
Problems
4h
Laboratory
2h
Guided learning
0h
Autonomous learning
15h

Development of the third subject's block: Learning



Contents:
Theory
4h
Problems
4h
Laboratory
2h
Guided learning
0h
Autonomous learning
15h

Test


Objectives: 1
Week: 13
Theory
0h
Problems
2.5h
Laboratory
0h
Guided learning
0h
Autonomous learning
15h

Development of the fourth subject's block: Trends and applications



Theory
1h
Problems
1h
Laboratory
0.5h
Guided learning
0h
Autonomous learning
0h

Students' presentations


Objectives: 1
Week: 15
Theory
2.5h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
15h

Teaching methodology

Lectures dynamically combine master explanations and problem solving. The weekly schedule of in-person activities is distributed in three hours. Some slots may be exclusively dedicated to programming throughout directed activities or notebooks.
The students will be required to present an application of PGMs (their own or other people's) to problems of their interest, or a recently proposed PGM technique.

Evaluation methodology

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

Bibliography

Basic:

Web links

Previous capacities

The subject requires the student to have basic knowledge of linear algebra and calculus, and be familiar with basic probability concepts.