Intelligent Systems

Universidad de Deusto - Bilbao

Course Description

  • Course Name

    Intelligent Systems

  • Host University

    Universidad de Deusto - Bilbao

  • Location

    Bilbao, Spain

  • Area of Study

    Information Sciences, Information Technologies

  • Language Level

    Taught In English

  • Prerequisites

    Programming concepts. UML notation for the design of class diagrams. Algorithmics, data structures and object oriented programming. JAVA programming language.

    Hours & Credits

  • Contact Hours

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    In this course on Intelligent Systems, emphasis is placed on solving difficult problems,
    many of them NP-complete, by means of designing and using heuristics for artificial
    intelligence algorithms, and by developing knowledge based systems.

    So, students will learn to formulate search problems and to identify and apply an
    appropriate solving technique. They will also be able to define and apply good
    heuristics to solve different problems considered difficult. Besides, they will learn to
    apply machine learning techniques as a way for an intelligent system to gain a certain
    degree of autonomy. Finally, students will learn to analyze problems whose resolution
    requires empirical knowledge and to design knowledge-based systems.

    Chapter 1. What is Artificial Intelligence?: Definitions of Artificial Intelligence. The
    Foundations of Artificial Intelligence. Application areas of Artificial Intelligence. Abridged
    history of Artificial Intelligence.
    Chapter 2. Intelligent Systems: The Concept of Rationality. Problem Environment. Properties of
    problem environments. Problem environment and performance measure. Types of problems
    addressed by Intelligent Systems.
    Chapter 3. Search and Heuristics: Solving problems by search techniques. Uninformed, or blind,
    search. Informed, or heuristic, search. How to define good heuristics and their application. Local
    search. On-line search. Adversarial Seach. Constraint Satisfaction Problems.
    Chapter 4. Machine Learning: The definition of learning within the Artificial Intelligence
    context. Supervised Learning. Regression and Classification. Linear Regression. Decision Tree
    Chapter 5. Knowledge Based Systems: Knowledge representation. Knowledge representation
    techniques. Inference and reasoning. Development of knowledge based systems that combine
    objects and rules. Forward chaining rule systems. Backward chaining rule systems.

    The course includes the following activities :
    - Presentation and debate
    - Solving exercises, problems and cases.
    - Group projects applying the case solving method.
    - Programming tasks
    - Personal reading and study

    Group projects: 35%
    Exam: 60%
    Individual activities: 5%

    Russell, S. & Norving, P. Artificial Intelligence: A modern approach. 3ª Ed. Prentice-Hall.
    Learning material accessible on the on-line platform for learning: Course programme and
    learning guide. Slides. Exercises and solutions. Problem Cases. Exams from previous years.
    Programming code templates and program samples. Instructions and complementary
    documentation for the different learning activities and group projects. Forums to answer questions. Links to especialized web pages and recommended reading.

Course Disclaimer

Courses and course hours of instruction are subject to change.

Eligibility for courses may be subject to a placement exam and/or pre-requisites.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.