Processes of Intelligent Data Analysis

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
EIO
Intelligent Data Analysis Processes is the fourth course in a sequence of courses where in which the rudiments of probability, statistical inference and statistical modelling have been acquired. This course culminates the training to bring data to more complex decision-making, with an in-depth study of the design of comprehensive processes that incorporate data and use various forms of artificial intelligence and advanced data models to extract strategic value from them. , while connecting the results of the data models with other components of the decision systems and processes. In this subject, the techniques seen in large part of the subjects of the preceding subjects such as "Probability and Statistics", "Intelligent Data Analysis", "Machine Learning", "Knowledge, Automatic Reasoning" and "Knowledge-Based Systems" and "Treatment of Human Language" will be seen as parts of more complex analysis processes, ranging from data collection to the integration of data and knowledge-based models in comprehensive decision support systems or different schemes for integrating AI and data in decisions.

Teachers

Person in charge

Others

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
6

Competences

Transversal Competences

Transversals

  • CT4 [Avaluable] - Teamwork. Be able to work as a member of an interdisciplinary team, either as a member or conducting management tasks, with the aim of contributing to develop projects with pragmatism and a sense of responsibility, taking commitments taking into account available resources.
  • CT6 [Avaluable] - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.
  • CT8 [Avaluable] - Gender perspective. An awareness and understanding of sexual and gender inequalities in society in relation to the field of the degree, and the incorporation of different needs and preferences due to sex and gender when designing solutions and solving problems.

    • Basic

  • CB2 - That the students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of ??study.
  • CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.
  • CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.
  • CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy

    • Technical Competences

    Especifics

  • CE09 - To ideate, design and integrate intelligent data analysis systems with their application in production and service environments.
  • CE17 - To develop and evaluate interactive systems and presentation of complex information and its application to solving human-computer and human-robot interaction design problems.
  • CE18 - To acquire and develop computational learning techniques and to design and implement applications and systems that use them, including those dedicated to the automatic extraction of information and knowledge from large volumes of data.
  • CE20 - To select and put to use techniques of statistical modeling and data analysis, assessing the quality of the models, validating and interpreting.

    • Generic Technical Competences

    Generic

  • CG1 - To ideate, draft, organize, plan and develop projects in the field of artificial intelligence.
  • CG2 - To use the fundamental knowledge and solid work methodologies acquired during the studies to adapt to the new technological scenarios of the future.
  • CG3 - To define, evaluate and select hardware and software platforms for the development and execution of computer systems, services and applications in the field of artificial intelligence.
  • CG4 - Reasoning, analyzing reality and designing algorithms and formulations that model it. To identify problems and construct valid algorithmic or mathematical solutions, eventually new, integrating the necessary multidisciplinary knowledge, evaluating different alternatives with a critical spirit, justifying the decisions taken, interpreting and synthesizing the results in the context of the application domain and establishing methodological generalizations based on specific applications.
  • CG5 - Work in multidisciplinary teams and projects related to artificial intelligence and robotics, interacting fluently with engineers and professionals from other disciplines.
  • CG7 - To interpret and apply current legislation, as well as specifications, regulations and standards in the field of artificial intelligence.
  • CG8 - Perform an ethical exercise of the profession in all its facets, applying ethical criteria in the design of systems, algorithms, experiments, use of data, in accordance with the ethical systems recommended by national and international organizations, with special emphasis on security, robustness , privacy, transparency, traceability, prevention of bias (race, gender, religion, territory, etc.) and respect for human rights.
  • CG9 - To face new challenges with a broad vision of the possibilities of a professional career in the field of Artificial Intelligence. Develop the activity applying quality criteria and continuous improvement, and act rigorously in professional development. Adapt to organizational or technological changes. Work in situations of lack of information and / or with time and / or resource restrictions.

    • Objectives

    1. Solving available open data sources in combination with private data
      Related competences: CG8, CT6, CT8, CB3,
    2. Identify what kind of preprocessing real data needs
      Related competences: CG4, CG8,
    3. Know methods of integrated analysis of data and knowledge and be able to apply them correctly to a real problem
      Related competences: CG2, CG4, CE18,
    4. Given a problem, data and perspectives for using the model, knowing how to choose the best model to apply among all those seen in the subject and in the previous ones
      Related competences: CG1, CG4, CG8, CT4, CT8, CB5, CE09, CE18, CE20,
    5. Combine the results of data-driven models with useful knowledge production methods for subsequent decision-making
      Related competences: CT4, CB2, CE09, CE17, CE18,
    6. Identify the reporting or visualization tools most suitable for a specific problem.
      Related competences: CB4,
    7. Integrate the tools and models that are known in the design of an intelligent data analysis process suitable for a specific problem.
      Related competences: CG2, CG3, CG4, CG9,
    8. Master the technologies of putting into production an intelligent data analysis process.
      Related competences: CG3, CG7, CG9, CE18,
    9. Be aware of AI's digital footprint and be able to apply strategies that reduce it in a process of intelligent data analysis.
      Related competences: CG2, CG3, CG8, CE09, CE18,
    10. Integrate intelligent data analysis processes into intelligent decision support system architectures.
      Related competences: CG1, CG3, CG4, CG5, CG8, CG9, CT4, CT6, CT8, CB2, CE09, CE20,
    11. Being able to document new methods or technologies autonomously
      Related competences: CT6,
    12. Understand the ethical principles of the current AI model and assess whether we can implement it in the debate.
      Related competences: CG4, CG8, CG9, CT8, CB4,
    13. Be able to document yourself about new methods or technologies independently and be able to self-train in the future.
      Related competences: CG5, CT6,

    Contents

    1. Introduction. The insertion of the data in the real decision processes
      Introducción a la teoría de la decisión y a los procesos reales de soporte a la toma de decisiones.
    2. Intelligent decision-making support systems
      Intelligent decision-making support systems. General purpose architecture for IDSS
    3. Intelligent decision-making support systems
      Intelligent decision-making support systems
    4. Design of relevant data sources for a decision-making process
      The relevant sources of information (data, images, videos, knowledge); static/dynamic; on-line/off-line; open, sample, experimental data. Primary/Secondary data.
      Linking the data with the objectives of the study. Data representativeness, biases and compensation policies
      Best practices from design.
    5. Integrated preprocessing design
      Construction of data preprocessing organizational charts for complex projects
      Role of study objectives and data models to be trained in data preprocessing processes
    6. Automated choice of data modeling methods for the decision support process
      -Integration of the DMMCM map in the method selection process
      -The DMMT model of representation of the data-based methods
      -Relation between the available methods and the objectives of the study
      -Relation between the available methods and the available data
      -Relation between different data-driven models
      -Relation between the available method and the intended use of the model
    7. Criteria to determine knowledge models
      Criteria for determining the knowledge representation models to integrate in the decision process (ontologies, knowledge bases, linguistic labels, etc.)
      Relationship between knowledge components/reasoning engines and data-based models in the decision support process
    8. Mixed data/knowledge-driven AI models in IDSS
      Mixed data/knowledge-driven models. Hybrid Artificial Intelligence systems.
    9. Impact of interface design in IDSS
      IDSS inputs, perception, knowledge representation in system inputs, access (roles, authentication, permissions), digital gap, modes of user interaction (voice, forms, chatbots, etc.). Good practices in menu design, accessibility, multilingual systems. Data connection (lakes, APIS, SQL, scrapping...).
      Outputs: applications of data visualization to an IDSS, explainability and argumentation, recommenders, automatic reporting, communication of metrics and KPIS, accountability (registers), system role (agency, assistance/automation)
      Validation of an IDSS
    10. Insertion of intelligent data analysis in real processes
      Health and wellness systems
      Business (retailing, negotiations, etc.)
      Administrative processes: public administration, hospital administration, large corporations, etc.
      Industry 4.0
      Strategic decision-making (business strategies or public policy-making)
      Sustainability (biodiversity, carbon footprint, etc.)

    Activities

    Activity Evaluation act


    Introduction to practices and training of work teams

    Introduction to practices and training of work teams

    Theory
    0h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    0h

    Introduction The insertion of the data in the real decision processes


    Objectives: 2
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    2h

    Generation of the AI Canvas from a real case

    Once the topic of the practical assignment has been chosen, define the Artificial Intelligence project that can be outlined using the CANVAS-AI methodology
    Objectives: 4
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    1.5h
    Guided learning
    0h
    Autonomous learning
    0h

    Intelligent decision-making support systems

    Design the system architecture
    Objectives: 2 3 4
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    1.5h
    Guided learning
    0h
    Autonomous learning
    6h

    Design of relevant data sources for a decision-making process


    Objectives: 1 5
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    1.5h
    Guided learning
    0h
    Autonomous learning
    8h

    Integrated preprocessing design


    Objectives: 6 7 8
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    1.5h
    Guided learning
    0h
    Autonomous learning
    6h

    Choice of data modeling methods for the decision support process


    Objectives: 2 3 4
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    1.5h
    Guided learning
    0h
    Autonomous learning
    10h

    Determination of knowledge models


    Objectives: 2 4 5 6 8
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    1.5h
    Guided learning
    0h
    Autonomous learning
    8h

    Other components of the decision-making and integration process


    Objectives: 10 11 13
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    6h

    Intermediate presentation of the practical works


    Objectives: 10
    Week: 8 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    User model and input interfaces in IDSS


    Objectives: 1 7 8
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    6h

    Design of the IDSS outputs and model explainability


    Objectives: 5 6 7
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    6h

    Global validation and deployment plan of the IDSS


    Objectives: 3 5 7 8
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    8h

    Ethical considerations and the carbon footprint of AI


    Objectives: 9 12
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    6h

    Final presentations of the practices



    Theory
    3h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    8h

    Presentation of real-world IDSS applications



    Theory
    0h
    Problems
    0h
    Laboratory
    3h
    Guided learning
    0h
    Autonomous learning
    2h

    Teaching methodology

    The 12 suggested topics will be developed in 12 theoretical class sessions (2 hours per week) with their respective practices or associated laboratory session (also 2 hours per week).

    The 3 sessions that are missing from the 15 sessions per semester established in the FIB, will be used for theoretical evaluations (quiz or similar) and practical evaluations (defense of practical work in the middle of the semester and at the end of the semester), remembering also that there are a couple of non-teaching weeks to be mid-term and/or final exam week, during which advice, support and guidance can be offered to students as reinforcement or preparation for their assessments.

    In the theory classes, the inverted class scheme will be practiced whenever possible.
    There is a web page for the subject.
    The temporary distribution of the subject's contents and the materials to be brought prepared before each class will be published on this platform(s).
    The master class outline will be used on occasion when the teacher needs to clarify complex concepts that have not been clear with the materials previously distributed in class.
    The theory class will be mainly devoted to the presentation of cases and the development of interactive activities with the students such as the discussion of the cases, or the completion of specific short questionnaires.
    One of the activities of the theory classes of the course will be the approach of real cases with proposals for the design of the intelligent data system to support certain decisions and the open discussion in the classroom about the strengths and weaknesses of the proposed design. This activity is fundamental to train the student in designing solvent, safe, viable processes with little risk of bankruptcy when we talk about real environments. Methodological questions to be clarified by the teacher will derive from the result of the debate.

    Additionally, the students will perform in groups a good number of short practical works on the design of intelligent data analysis processes in more or less mature scenarios from a technological point of view where the entire process will have to be done from the eventual collection or identification of data sources or knowledge up to the communication of results and recommendations with the user.
    The analysis case can be proposed by the students themselves based on certain characteristics set by the teaching staff. Each team will carry out practice sessions, each week applying the techniques seen in the course to tackle the challenge. The teacher will monitor all the work teams weekly in the laboratory sessions. The design proposal will include a proof of concept as far as the means of the subject allow for the proposed proposal.

    Twice a year the teams will present their proposals in a sharing session where all the projects will be discussed together.


    Supporting material resources include:
    * Slides/Transparencies for each subject in pdf format or similar.
    * Links to articles, forums, discussions or practical cases in congruent and reliable repositories for the subject.
    * Videos or similar to show case studies or complementary topics to master classes.
    * Use of GNU software for the practical part. The use of R, RStudio and similar platforms is suggested.
    * You can use specialized software developed by research groups within the UPC such as GESCONDA and Klass, Freeling, etc.

    Evaluation methodology

    The following evaluation system is proposed:
    - 4 Team works carried out throughout the course 80%.

    Each team work is evaluated
    - Technical quality of the proposed design and integration of knowledge involved (30%)
    - Proof of concept (20%)
    - Oral knowledge control test 10% (discussion with the teaching staff during the oral presentation of team work).
    - Quality and performance of the work team. 10%
    - Oral and written communication 10%.
    - Ethics of the work team and the work itself 10%
    -Gender perspective of the team and the work 10%.


    - Attendance and participation in classes and laboratories. 10%

    Reevaluación: Solo se pueden presentar a la reevaluación las personas que, habiéndose presentado al examen final, hayan suspendido. La máxima calificación que se puede alcanzar en la reevaluación es un 7.
    - 2 Quiz throughout the course 10% (5% each).

    Bibliography

    Basic

    Complementary

    Previous capacities

    In this subject the techniques seen in a large part of the subjects of the preceding subjects such as "Probability and Statistics", "Intelligent Data Analysis", "Machine Learning", "Logic, Automatic Reasoning and "Knowledge-Based Systems" and " Human language processing and perception"