Análisis de Datos y Descubrimiento de Conocimiento

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Créditos
6
Tipos
Obligatoria de especialidad (Ciencia de los Datos)
Requisitos
Esta asignatura no tiene requisitos, pero tiene capacidades previas
Departamento
CS
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This exciting course broaches the hot topic of Data Analysis and Knowledge Discovery (DAKD) from the viewpoint of Data Mining.
Most areas in science, engineering and business are becoming increasingly data dependent. Clear examples of this are, to name a few, bioinformatics, medicine, or electronic commerce.
Data analysis techniques are needed to deal with these data and generate usable knowledge out of them. Amongst them, DAKD techniques are one of the most promising approaches. This theme is at the core of the contents of this course.

Profesores

Responsable

  • Alfredo Vellido Alcacena ( )

Otros

  • Luis Antonio Belanche Muñoz ( )
  • Mario Martín Muñoz ( )

Horas semanales

Teoría
3
Problemas
0
Laboratorio
0
Aprendizaje dirigido
0.2
Aprendizaje autónomo
5

Competencias

Competencias Técnicas Genéricas

Genéricas

  • CG3 - Capacidad para el modelado matemático, cálculo y diseño experimental en centros tecnológicos y de ingeniería de empresa, particularmente en tareas de investigación e innovación en todos los ámbitos de la Informática.

Competencias Transversales

Uso solvente de los recursos de información

  • CTR4 - Gestionar la adquisición, la estructuración, el análisis y la visualización de datos e información del ámbito de la ingeniería informática y valorar de forma crítica los resultados de esta gestión.

Razonamiento

  • CTR6 - Capacidad de razonamiento crítico, lógico y matemático. Capacidad para resolver problemas dentro de su área de estudio. Capacidad de abstracción: capacidad de crear y utilizar modelos que reflejen situaciones reales. Capacidad de diseñar y realizar experimentos sencillos, y analizar e interpretar sus resultados. Capacidad de análisis, síntesis y evaluación.

Competencias Técnicas de cada especialidad

Específicas comunes

  • CEC1 - Capacidad para aplicar el método científico en el estudio y análisis de fenómenos y sistemas en cualquier ámbito de la Informática, así como en la concepción, diseño e implantación de soluciones informáticas innovadoras y originales.
  • CEC3 - Capacidad para aplicar soluciones innovadoras y realizar avances en el conocimiento que exploten los nuevos paradigmas de la Informática, particularmente en entornos distribuidos.

Objetivos

  1. Presenting DM as a process that should involve a methodology id applied at its best.
    Competencias relacionadas: CG3, CTR4, CTR6,
  2. Introducing the students to the new concept of DM for processes, called Process Mining.
    Competencias relacionadas: CG3, CEC3, CTR6,
  3. Delving into some detail in one of the stages of DM: data exploration.
    Competencias relacionadas: CTR4, CTR6,
  4. Dealing in detail with the problem of data visualization for exploration as a key issue in DM.
    Competencias relacionadas: CTR4, CTR6,
  5. Introducing the students to the basics of probability theory as applied in Data Analysis and Knowledge Discovery (DAKD)
    Competencias relacionadas: CEC1, CTR4, CTR6,
  6. Introducing the students to the probabilistic variant of DAKD in the form of Statistical Machine Learning, both for supervised and unsupervised learning models.
    Competencias relacionadas: CEC1, CTR4, CTR6,
  7. Dealing in detail with different unsupervised models for data visualization, including case studies.
    Competencias relacionadas: CG3, CTR4, CTR6,
  8. Approaching the multi-faceted concept of data mining (DM) from different perspectives.
    Competencias relacionadas: CG3, CTR4,

Contenidos

  1. Introduction to the concept of data mining (DM).
    DM is a multi-faceted concept that requires discussion and clarification. We will do this at the beginning of the course.
  2. DM as a methodology.
    We argue that DM should not be focused on the concept of data analysis/modeling, but, instead, should be treated as a methodology with diverse inter-related stages.
  3. DM for processes: Process Mining.
    A new development in DM methodologies is that which deals with one specifically suited for processes. It is called Process Mining and will be described and discussed in this course.
  4. Data exploration in DM.
    One of the main stages of well-structures DM methodologies is Data exploration. It will be discussed as a preamble to data visualization.
  5. Data visualization for exploration.
    One of the aspects of the problem of data exploration is data visualization. It has a research 'life' of its own as it involves not only computer-based mathematical models, but also natural perception and processing.
  6. Basics of probability theory in Data Analysis and Knowledge Discovery (DAKD)
    For a long time in the last half-century, multivariate statistics and artificial intelligence (mostly in the field of machine learning) have developed in parallel without fully meeting. Statistical machine learning has bridged that field over the last two decades. We introduce it by first providing some basic principles of probability theory (Bayesian inference).
  7. Statistical Machine Learning for DAKD: supervised models.
    Once the basics of Bayesian inference are set, we will delve into the field of Statistical Machine Learning for IDA, starting with supervised learning models, with an emphasis on feed-forward artificial neural networks.
  8. Statistical Machine Learning for DAKD: unsupervised models.
    Once the basics of Bayesian inference and of Statistical Machine Learning for IDA in supervised models are set, we will continue with unsupervised models, focusing on self-organizing maps and related models.
  9. Unsupervised models for data visualization, with case studies.
    In the final item of the contents of the course, we will bring statistical machine learning and data visualization together by discussing some probabilistic unsupervised learning models for data visualization, including some case studies as an example.

Actividades

Actividad Acto evaluativo


Essay on DAKD for DM

Students will have to write a research essay on the topic of DAKD for DM, with different options: 1. State of the art on an specific DAKD-DM topic 2. Evaluation of an DAKD-DM software tool with original experiments 3. Pure research essay, with original experimental content
Objetivos: 1 2 3 4 5 6 7 8
Semana: 18
Tipo: entrega
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
3h
Aprendizaje autónomo
0h

Introduction to Data Mining and its Methodologies

Introduction to Data Mining as a general concept and to its methodologies for practical implementation
Objetivos: 1
Contenidos:
Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
1h
Aprendizaje autónomo
13h

Process Mining

Introduction to the novel concept of Process Mining and its application within the DM framework.
Objetivos: 2
Contenidos:
Teoría
3h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
1h
Aprendizaje autónomo
5h

Data Visualization

As part of the DM stage of Data Exploration, we focus in the problem of Data Visualization.
Objetivos: 3 4
Contenidos:
Teoría
6h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
1h
Aprendizaje autónomo
9h

Basics of probability theory for intelligent data analysis

Introduction to probability theory for intelligent data analysis, with a focus on Bayesian statistics
Objetivos: 5
Contenidos:
Teoría
6h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
1h
Aprendizaje autónomo
9h

Statistical Machine Learning methods

The meeting of statistics and machine learning: Statistical Machine Learning methods, from the point of view of both supervised and supervised learning
Objetivos: 5 6
Contenidos:
Teoría
12h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
1h
Aprendizaje autónomo
18h

SML in data visualization, with case studies

We merge the topics of SML and data visualization, illustrating its use with some real case studies
Objetivos: 4 7 8
Contenidos:
Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
1h
Aprendizaje autónomo
15h

Metodología docente

This course will build on different teaching methodology (TM) aspects, including:
TM1: Expositive seminars
TM2: Expositive-participative seminars
TM3: Orientation for individual assignments (essays)
TM4: Individual tutorization

Método de evaluación

The course will be evaluated through a final essay that will take one of these three modalities:
1. State of the art on an specific IDA-DM topic
2. Evaluation of an IDA-DM software tool with original experiments
3. Pure research essay, with original experimental content

Bibliografía

Básica:

Complementaria:

Capacidades previas

Students are expected to have at least some basic background in the area of artificial intelligence and, more specifically, with the areas of Machine Leaning and Computational Intelligence.
Some basic knowledge of probability theory and statistics would be beneficial.
Other than this, the course is open to students and researchers of all types of background.

Adenda

Contenidos

No modification of the course contents will be necessary in case of switching to remote access to the course due to an eventual Covid-19 related emergency.

Metodología docente

No major modification of the course methodology will be necessary in case of switching to remote access to the course due to an eventual Covid-19 related emergency. The only modifications would be those directly associated to the limitations of video-conferencing.

Método de evaluación

No modification of the course evaluation method will be necessary in case of switching to remote access to the course due to an eventual Covid-19 related emergency.

Plan de contingencia

In case of switching to remote access to the course due to an eventual Covid-19 related emergency, classes will be delivered through Google Meet-based video-conferences, using UPC identity for secure access.