Algorithmics for Data Mining

Credits
6
Types
  • MDS: Elective
  • MIRI: Specialization complementary (Advanced Computing)
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS
Data mining is the process of extracting and discovering patterns in (usually) large data sets involving methods at the intersection of machine learning, multivariate statistics and database systems. Nowadays it uses these methods in a principled way to form an end-to-end process, from a raw data set to high-level information, expressed into a comprehensible structure for the final user.

The goal of this course is to present and study some of the most widespread, useful and elegant algorithms at the intersection of these three disciplines, so that students become capable of identifying and applying the suitable tools for a given application. The lectures will cover the theory, algorithms and practical usage of the techniques.

Teachers

Person in charge

Weekly hours

Theory
1
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
6.375

Competences

Technical Competences of each Specialization

Advanced computing

  • CEE3.1 - Capability to identify computational barriers and to analyze the complexity of computational problems in different areas of science and technology as well as to represent high complexity problems in mathematical structures which can be treated effectively with algorithmic schemes.
  • CEE3.2 - Capability to use a wide and varied spectrum of algorithmic resources to solve high difficulty algorithmic problems.
  • CEE3.3 - Capability to understand the computational requirements of problems from non-informatics disciplines and to make significant contributions in multidisciplinary teams that use computing.

    • Generic Technical Competences

    Generic

  • CG1 - Capability to apply the scientific method to study and analyse of phenomena and systems in any area of Computer Science, and in the conception, design and implementation of innovative and original solutions.
  • CG3 - Capacity for mathematical modeling, calculation and experimental designing in technology and companies engineering centers, particularly in research and innovation in all areas of Computer Science.
  • CG5 - Capability to apply innovative solutions and make progress in the knowledge to exploit the new paradigms of computing, particularly in distributed environments.

    • Transversal Competences

    Teamwork

  • CTR3 - Capacity of being able to work as a team member, either as a regular member or performing directive activities, in order to help the development of projects in a pragmatic manner and with sense of responsibility; capability to take into account the available resources.

    • Information literacy

  • CTR4 - Capability to manage the acquisition, structuring, analysis and visualization of data and information in the area of informatics engineering, and critically assess the results of this effort.

    • Appropiate attitude towards work

  • CTR5 - Capability to be motivated by professional achievement and to face new challenges, to have a broad vision of the possibilities of a career in the field of informatics engineering. Capability to be motivated by quality and continuous improvement, and to act strictly on professional development. Capability to adapt to technological or organizational changes. Capacity for working in absence of information and/or with time and/or resources constraints.

    • Reasoning

  • CTR6 - Capacity for critical, logical and mathematical reasoning. Capability to solve problems in their area of study. Capacity for abstraction: the capability to create and use models that reflect real situations. Capability to design and implement simple experiments, and analyze and interpret their results. Capacity for analysis, synthesis and evaluation.

    • 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.
  • 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.
  • CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

    • Objectives

    1. Te be aware of the theoretical and practical set of problems that constitute Data Mining, and to understand the main models and algorithms to tackle it: both at the conceptual level and at the level of their application through commercial tools, preferably open-source.
      Related competences: CB6, CTR4, CTR5, CTR6, CEE3.1, CEE3.2, CEE3.3, CG1, CG3, CG5,
    2. To acquire and demonstrate an ability to put to work the knowledge obtained in the autonomous, team-wise deployment of a practical data mining case, including a public presentation of the work developed.
      Related competences: CB6, CB8, CB9, CTR3, CTR4, CTR5, CTR6, CEE3.2, CG3,

    Contents

    1. Selected techniques and algorithms for Data Mining
      Algorithms and techniques are representative of the good and the best a data practitioner needs to know, among which:

      backpropagation
      expectation-maximization
      association rules
      pagerank
      GLMs

      Each topic of study is focused in 3 aspects:

      theoretical
      algorithmic
      practical

    Activities

    Activity Evaluation act


    Theoretical and conceptual study of the main data mining algorithms.

    Theoretical and conceptual study of the main data mining algorithms.
    Objectives: 1
    Contents:
    Theory
    18h
    Problems
    6h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    6h

    Deploy of a practical case study

    Deploy of a practical case study
    Objectives: 1 2
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    36h
    Guided learning
    0h
    Autonomous learning
    18h

    Teaching methodology

    Theoretical classes, exercises and problems in data analysis with or without a programming component and development of case studies.

    Evaluation methodology

    The course grade is based on five components:

    Project 1 (Sessions 1-5) 30% Group
    Project 2 (Sessions 6-10) 30% Group
    Control 1 10% Individual
    Control 2 10% Individual
    Final exam 20% Individual

    Bibliography

    Basic

    Previous capacities

    Adequate understanding of computing in general, especially algorithms; good level of various programming languages (such as R, python, Julia) or willingness to achieve it; basic to average ability to mathematically formalize concepts in computing, statistics, etc.