Program Overview

University Programs

The university offers a range of programs for students, including bachelor's degrees, master's degrees, and integrated bachelor-master degrees.

Bachelor's Degrees

• Bachelor Degree in Informatics Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Reassessment, Specializations, Competences, Competences for degree subjects
  • Faculty
  • Bachelor's Thesis
  • Timetables
  • Exams
  • Academic Regulations
• Bachelor Degree in Data Science and Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
  • Timetables
  • Exams
  • Academic Regulations and organization
• Bachelor Degree in Artificial Intelligence
  • Enrolment: Places lliures
  • Curriculum: Competences, Syllabus, Competences for degree subjects
  • Faculty
  • Timetables
  • Exams
  • Academic regulations
  • Bachelor's thesis
• Bachelor Degree in Bioinformatics
  • Enrolment: Available places
  • Curriculum: Learning Outcomes, Syllabus
  • Faculty
  • Timetables
  • Exams
  • Academic Regulations
• Integrated Bachelor Master Degree
  • Enrolment
  • Curriculum

Master's Degrees

• Master in Informatics Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
  • Academic Regulations
  • Master's Thesis
  • Timetables
  • Exams
• Master in Informatics Engineering - Industrial Modality
  • Curriculum
• Master in Innovation and Research in Informatics
  • Enrolment: Available places
  • Curriculum: Syllabus, Specializations, Competences, Competences for degree subjects
  • Faculty
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  • Master's Thesis
  • Seminars
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• Master in Artificial Intelligence
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
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  • Master's Thesis
  • Timetables
  • Exams
  • FAQs
• Master in Cybersecurity
• Master in Data Science
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
  • Academic Regulations
  • Timetables
  • Exams
  • Master's Thesis
    • Gender Competency
• Erasmus Mundus Master in Big Data Management and Analytics
  • Timetables
  • Curriculum: Syllabus
  • Exams
• Master in Urban Mobility
  • Curriculum
• EUMaster4HPC
  • Curriculum
• Other Masters
  • Master in Pure and Applied Logic
  • Master in Computational Modelling in Physics, Chemistry and Biochemistry

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Course Details: Intelligent Data Analysis and Data Mining

Credits and Type

• Credits: 4.5
• Type: Elective

Requirements

This subject has no requirements but has previous capacities.

Department

• CS

Course Description

This course covers the topic of Intelligent Data Analysis (IDA) from the viewpoint of Data Mining. It deals with the hot topic of IDA, focusing on data-dependent areas such as bioinformatics, medicine, and electronic commerce. The course aims to provide students with the knowledge and skills necessary to analyze and generate usable knowledge from data.

Teachers

• Person in charge: Alfredo Vellido Alcacena
• Others: Carlos Cano Domingo, Caroline König

Weekly Hours

• Theory: 2.9
• Problems: 0
• Laboratory: 0
• Guided learning: 0.1
• Autonomous learning: 4.6

Competences

Generic Technical Competences

• CG3: Capacity for modeling, calculation, simulation, development, and implementation in technology and company engineering centers.

Technical Competences of each Specialization

• CEA4: Capability to understand the basic operation principles of Computational Intelligence main techniques.
• CEA7: Capability to understand the problems and solutions in the professional practice of Artificial Intelligence application.
• CEA11: Capability to understand advanced techniques of Computational Intelligence.

Professional Competences

• CEP1: Capability to solve the analysis of information needs from different organizations.
• CEP5: Capability to design new tools and techniques of Artificial Intelligence in professional practice.

Transversal Competences

• CT4: Capacity for managing the acquisition, structuring, analysis, and visualization of data.
• CT6: Capability to evaluate and analyze situations, projects, and proposals critically.
• CT7: Capability to analyze and solve complex technical problems.

Objectives

1. Presenting Data Mining as a process involving a methodology.
2. Introducing Process Mining.
3. Delving into data exploration.
4. Dealing with data visualization for exploration.
5. Introducing probability theory for Intelligent Data Analysis.
6. Introducing Statistical Machine Learning for IDA.
7. Discussing unsupervised models for data visualization.
8. Approaching the concept of data mining from different perspectives.

Contents

1. Introduction to Data Mining.
2. DM as a methodology.
3. DM for processes: Process Mining.
4. Data exploration in DM.
5. Basics of probability theory in IDA.
6. Data visualization for exploration.
7. Statistical Machine Learning for IDA: supervised and unsupervised models.
8. Unsupervised models for data visualization, with case studies.

Activities

• Essay on IDA for DM
• Introduction to Data Mining and its methodologies
• Process Mining
• Data Visualization
• Basics of probability theory for IDA
• Statistical Machine Learning methods
• SML in data visualization, with case studies

Teaching Methodology

The course will use various teaching methodologies, including expositive seminars, expositive-participative seminars, orientation for individual assignments, and individual tutorization.

Evaluation Methodology

The course will be evaluated through a final essay that can take one of three modalities: state of the art on a specific IDA-DM topic, evaluation of an IDA-DM software tool with original experiments, or a pure research essay with original experimental content.

Bibliography

• Information theory, inference, and learning algorithms by MacKay, D.J.C.
• Principles of data mining by Hand, D.; Mannila, H.; Smyth, P.
• Pattern recognition and machine learning by Bishop, C.M.

Previous Capacities

Students are expected to have a basic background in artificial intelligence, machine learning, and computational intelligence. Some knowledge of probability theory and statistics is beneficial.