Intelligent System Project

Credits
3
Types
Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS
Mail
assig-ISP-MAI@fib.upc.edu
The aim of this course is the construction of an Intelligent System to perform a non-trivial task. The development of an Intelligent System shares many steps with the development of any software system. Notwithstanding, there are some special features, such as the knowledge acquisition step, task analysis, selection of Intelligent methods, and integration of Intelligent techniques, that are specific to Intelligent System projects. The IS project is a project like those ones that students will cope with, in their professional practice of Artificial Intelligence in any company. The project will be constructed by a team of three or four students.

Teachers

Person in charge

Others

Competences

Generic Technical Competences

Generic

  • CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.

    • 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.

    • Professional

  • CEP4 - Capability to design, write and report about computer science projects in the specific area of ??Artificial Intelligence.
  • CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.
  • CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.

    • Transversal Competences

    Sustainability and social commitment

  • CT2 - Capability to know and understand the complexity of economic and social typical phenomena of the welfare society; capability to relate welfare with globalization and sustainability; capability to use technique, technology, economics and sustainability in a balanced and compatible way.

    • Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

    • 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.
  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.

    • Objectives

    1. The students will be able to integrate and apply several knowledge acquired in previous Master courses for the solving of complex problems using Artificial Intelligence techniques
      Related competences: CB6, CB7, CEA12,
    2. Students will be able to write and communicate their technical and research work on Intelligent Systems and achievements both to a general and specialized audience.
      Related competences: CB8, CEP4,
    3. Students will acquire and learn the concepts and knowledge related to sustainability and their intrinsic relationship with Intelligent Systems.
      Related competences: CT2, CEP8,
    4. Students will consolidate teamworking abilities.
      Related competences: CT3,
    5. Students will be able to design and construct an Intelligent System to solve a non trivial problem.
      Related competences: CT7, CEP5, CG1,

    Contents

    1. Introduction
      Description of the aims of the course.
      Description of the teamwork.
      Information about the IS project timeline.
      Deliverables of the IS project.
    2. Problem Analysis
      Problem Feature Analysis. Information/Data Analysis. Viability Analysis. Economical Analysis. Environmental and Sustainability Analysis.
    3. Definition of the Intelligent System project issues
      Definition of the main goals of the IS project. Definition of sub-goals. Task Analysis.
    4. Development of an AI-based System Project
      Data/Information Extraction. Data Mining & Knowledge Acquisition Process. Knowledge/Ontological Analysis. Planning and selection of Intelligent/Statistical/Mathematical Methods/Techniques. Construction of Models and Implementation of Techniques. Module Integration. Validation of Models/Techniques. Comparison of Techniques. Proposed Solution.
    5. Intelligent System Project Output
      Executive Summary. Project System Documentation: User's Manual, System Manual. Project Schedule (Gantt's Chart). The Project Time Sheet.
    6. Intelligent Methods and Models
      Review of the main Intelligent Methods and tools available.
    7. Software tools
      Review of the main AI-based software tools available.

    Activities

    Activity Evaluation act


    Introductory Lab Session

    First Lab class will focus on laboratory working teams and on giving information about the IS project. (timeline, deliverables, etc.)
    Objectives: 4
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    2h

    Lab Sessions on the analysis of the problem and the design and implementation of an Intelligent System Project

    The following classes will be dedicated to providing information about the process of developing an Intelligent System and all its phases.
    • Laboratory: Problem analysis and design and implementation of an Intelligent System Project.
    Objectives: 3 5
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    4h

    Laboratory sessions on the review of intelligent methods and intelligent software tools available


    • Laboratory: Session review of the main Artificial Intelligence models and software tools
    Objectives: 1
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    2h

    Laboratory sessions for tracking the project

    The remaining laboratory classes (7) are devoted to overseeing and guiding the various Intelligent Systems projects of the different groups.
    • Laboratory: Lab sessions for the development of the IS project
    Objectives: 5
    Theory
    0h
    Problems
    0h
    Laboratory
    14h
    Guided learning
    0h
    Autonomous learning
    0h

    Midterm Deliverable

    It is a document with the project analysis and project design at midterm of the project
    Objectives: 2
    Week: 8
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Final Presentation of the project

    The project developed will be orally presented in class by each team, and they will have previously submitted all the required documentation, as well as the corresponding software code.
    Objectives: 2 5
    Week: 15 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    Teaching Methods

    A variety of instructional methods will be used throughout the course to enhance learning and engagement:

    * Expository Lectures: Clear and structured presentations of theoretical concepts.
    * Interactive Lectures: Sessions are designed to encourage active participation and discussion.
    * Project Supervision: Hands-on guidance and mentoring during project development.
    * Workshops on Independent and Teamwork Skills: Focused sessions to support autonomous learning and effective collaboration.

    Course Structure

    First Session: The opening class will introduce students to laboratory teams and provide essential information about the main project.
    Development Sessions (Classes 2-4): These sessions will cover the complete process of developing an AI-based system, including all key phases and methodologies.
    Project Supervision Sessions (Classes 5-11): The remaining laboratory classes will be dedicated to supervising and supporting student groups as they design, implement, and refine their Intelligent System projects.

    This structure ensures a balanced combination of theory, practical guidance, and collaborative work, preparing students for both academic and real-world challenges in the development of AI-based systems.

    Evaluation methodology

    The assessment of the achievement of the objectives of the course will be made by assessing the achievements of an Intelligent System project throughout the course, which will be done working in teams of 3 or 4 students.

    The final grade (FGrade) is a weighted average between the teamwork (TGrade) assessment and the evaluation of the work of each individual student (IGrade) according to the formula:

    FGrade = 0.5 * TGrade + 0.5 * IGrade

    The individual grade for each student (IGrade) will be obtained as the mean between the observation and assessment of the ongoing work and participation of each student throughout the project according to the teacher (TeachA) and the self-assessment of each student participation and work in the team by all the team members including herself/himself (SelfA). Thus,

    IGrade = 0.5*TeachA+ 0.5*SelfA

    The teamwork grade (TGrade) will be a weighted average between four marks, corresponding to the four Milestones, related to the definition of the project document (MS1-D1Gr), the midterm delivery and oral exposition of system analysis and design (MS2-D2Gr) the final document and software delivery (MS3Gr = 0.5 * MS3-D3Gr + 0.5 * MS3-D4Gr), and the final public presentation of the project (MS4Gr). Thus, TGrade will be computed according to the formula:

    TGrade = 0.15 * MS1-D1Gr + 0.2 * MS2-D2Gr + 0.45 * MS3Gr + 0.2 * MS4Gr

    Bibliography

    Basic

    Complementary

    Web links

    Previous capacities

    The knowledge and abilities provided by the mandatory courses of the Master in Artificial Intelligence