Data Analysis and Knowledge Discovery

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Credits
6
Types
Specialization compulsory (Data Science)
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
Web
https://raco.fib.upc.edu/home/assignatura?espai=270650
Mail
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.

Teachers

Person in charge

  • Alfredo Vellido Alcacena ( )

Others

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

Weekly hours

Theory
3
Problems
0
Laboratory
0
Guided learning
0.2
Autonomous learning
5

Objectives

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

Contents

  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.

Activities

Activity Evaluation act


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
Objectives: 1 2 3 4 5 6 7 8
Week: 18
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
3h
Autonomous learning
0h

Introduction to Data Mining and its Methodologies

Introduction to Data Mining as a general concept and to its methodologies for practical implementation
Objectives: 1
Contents:
Theory
9h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
13h

Process Mining

Introduction to the novel concept of Process Mining and its application within the DM framework.
Objectives: 2
Contents:
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
5h

Data Visualization

As part of the DM stage of Data Exploration, we focus in the problem of Data Visualization.
Objectives: 3 4
Contents:
Theory
6h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
9h

Basics of probability theory for intelligent data analysis

Introduction to probability theory for intelligent data analysis, with a focus on Bayesian statistics
Objectives: 5
Contents:
Theory
6h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
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
Objectives: 5 6
Contents:
Theory
12h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
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
Objectives: 4 7 8
Contents:
Theory
9h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
15h

Teaching methodology

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

Evaluation methodology

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

Bibliography

Basic:

Complementary:

Previous capacities

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.