Computing and Intelligent Systems

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS
Web
www.cs.upc.edu\~larrosa/csi.html
This course will explain the basics of intelligent systems. These methods include mathematical foundations, algorithmic and statistical. The course is divided into three parts, covering the three "legs" basic intelligent systems:
- Knowledge representation and reasoning in environments with automatic certainty of
- knowledge representation and reasoning with uncertainty
- Machine-Learning System

Teachers

Person in charge

Others

Weekly hours

Theory
1
Problems
1
Laboratory
2
Guided learning
0
Autonomous learning
8.5

Competences

Technical Competences of each Specialization

Especifics

  • CTE1 - Capability to model, design, define the architecture, implement, manage, operate, administrate and maintain applications, networks, systems, services and computer contents.
  • CTE7 - Capability to understand and to apply advanced knowledge of high performance computing and numerical or computational methods to engineering problems.
  • CTE9 - Capability to apply mathematical, statistical and artificial intelligence methods to model, design and develop applications, services, intelligent systems and knowledge-based systems.

    • Transversal Competences

    Reasoning

  • CTR6 - Capacity for critical, logical and mathematical reasoning. Capability to solve problems in their area of study. Capacity for abstraction: the capability to create and use models that reflect real situations. Capability to design and implement simple experiments, and analyze and interpret their results. Capacity for analysis, synthesis and evaluation.

    • 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.

    • Objectives

    1. Get languages ​​for modeling and solving problems and reasoning saver and apply them to specific problems with certainty and uncertainty, using specialized tools, while being aware of the implications of complexity theory.
      Related competences: CB6, CTR6, CTE1, CTE7, CTE9,

    Contents

    1. Knowledge representation and reasoning in the context of automatic certainty
      You will see the MiniZinc modeling language. Syntax and semantics, basic inference algorithms and expressive ability.
    2. Knowledge representation and automatic reasoning with uncertainty
      Will be the Bayesian Networks, syntax, semantics, the basic inference algorithms and their expressive power.
    3. Machine learning
      Will be the most important machine learning algorithms understanding the strengths and weaknesses of each in order to know what is the most appropriate for each situation

    Activities

    Activity Evaluation act


    Development of the first theme of the course (propositional logic)

    Assimilate the basics of propositional logic (syntax, semantics, inference) understand the expressive power of propositional logic and see examples of actual use.
    Objectives: 1
    Contents:
    Theory
    5h
    Problems
    5h
    Laboratory
    10h
    Guided learning
    2h
    Autonomous learning
    25h

    2 Development of the subject matter (Bayesian networks)

    Assimilate the basics of Bayesian networks (syntax, semantics, inference) Assimilate the expressive power of Bayesian networks and examples of actual use.
    Objectives: 1
    Contents:
    Theory
    4h
    Problems
    4h
    Laboratory
    8h
    Guided learning
    2h
    Autonomous learning
    25h

    3 Develop the topic of the course (Machine Learning)


    Objectives: 1
    Contents:
    Theory
    4h
    Problems
    4h
    Laboratory
    8h
    Guided learning
    2h
    Autonomous learning
    25h

    Teaching methodology

    It combines lectures to introduce the fundamental concepts, the classes of problems to practice and exercise their implications with laboratory classes, where you will see a more practical all this through case study and using packages already implemented.

    Evaluation methodology

    The course is divided into 3 parts, each one with the same weight. Each part is evaluated with an exam and a project.

    Bibliography

    Basic

    Complementary