Probabilistic Graphical Models

Credits
4.5
Types
Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
UB;CS
Mail
jeronimo.hernandez@ub.edu
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: CB6, CB9, CT4, CT6, CT7, CEA12, CEA13, CEA3, CEA8, CEP5, CEP1, CEP2, CEP3, CG3,

    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
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    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
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    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.