abierta UGR

MOOC Machine Learning y Big Data para la Bioinformática. 2ª Edición

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Topic outline

  • GENERAL INFORMATION

    GENERAL INFORMATION

    Welcome to MOOC Machine Learning y Big Data para la Bioinformática". If you see this message, you are officially enrolled in it.

    (Read carefully all the information in this block, it is very important that you have all the doubts resolved before officially starting the course)


    • COMMUNICATION TOOLS

      COMMUNICATION TOOLS

      • Announcements Forum
        In this FORUM, News and Relevant Information of the MOOC will be published. Only teachers and tutors/facilitators will be able to write on it.

    • INFORMATION ON TPV (PAYMENT), OFFICIAL CERTIFICATION AND RECOGNITION OF CREDITS

      INFORMATION ON TPV (PAYMENT), OFFICIAL CERTIFICATION AND RECOGNITION OF CREDITS

      • CÓMO OBTENER EL CERTIFICADO OFICIAL

        Su navegador no puede reproducir este video. Pruebe con otro navegador.

    • REQUIREMENTS FOR SUCCESSFULLY COMPLETING THE MOOC

      REQUIREMENTS FOR SUCCESSFULLY COMPLETING THE MOOC

      This course is structured into 8 compulsory modules and its overall duration will be 8 weeks. To successfully pass the UGR’s Machine Learning and Big Data MOOC you must have obtained all 8 badges associated with each of the modules, as well as the final badge, as described below.
      1. The badge for each module is obtained by correctly completing (getting a passing score) the evaluation questionnaires associated with each of the modules. These tests will be opened on the Friday of the week in which the module is taught.
      2. The final badge for this MOOC is obtained by correctly completing the concluding questionnaire which will also allow you to obtain the official certificate for the course should you wish (subject to payment of the corresponding fees). All the questionnaires are compulsory and so they must be completed to correctly finish the MOOC. They will all be made available in this block. We hope you enjoy this new adventure in bioinformatics!

    • Module 1: What is bioinformatics?

      Module 1: What is bioinformatics?

      Carlos Cano Gutiérrez, Lecturer in computer science and artificial intelligence at the University of Granada and Module 1 coordinator, will introduce us to this first module, What is Bioinformatics?

      INFORMATION: Each learning unit consists of:A video unit and an Assessable content

      • 1.1.- What, why for and how?

      • 1.1. Bioinformatics: what, why, and how? Page
        Coral del Val Muñoz, Professor at the University of Granada. Department of Computer Science and Artificial Intelligence (DECSAI) and Carlos Cano Gutiérrez, Professor at the University of Granada. Department of Computer Science and Artificial Intelligence (DECSAI).

      • 1.2.- Applications in Bio-Sciences and Bio-Health

      • 1.2.- Applications in Bio-Sciences and Bio-Health Page
        Coral del Val Muñoz, Professor at the University of Granada. Department of Computer Science and Artificial Intelligence (DECSAI) and Pedro Carmona Sáez, Assistant Professor Doctor at the University of Granada. Department of Statistics and Operational Research, they will talk to us about the applications that Bioinformatics has in Bio-Science and Bio-Health.

      • 1.3.- Precision Medicine (Bioinformatics for the genomic interpretation of cancer)

      • Video 1.3 Page
        This Learning Unit has no evaluable content.

    • Module 2: Bioinformatics analysis of a problem in Omicas

      Module 2: Bioinformatics analysis of a problem in Omicas

      Carlos Cano Gutiérrez, Associate Professor in computer science and artificial intelligence at the University of Granada and Module 2 coordinator, will introduce us to a problem in Omics

      INFORMATION: Each learning unit consists of:A video unit and an Assessable content


    • Modulo 3: Data Science and Machine Learning

      Modulo 3: Data Science and Machine Learning

      Alberto Fernández Hilario, Senior Professor at the University of Granada, Andalusian Interuniversity Institute on Data Science and Computational Intelligence (DasCI); Coordinator of this third module, he will introduce us to Data Science and Machine Learning.

      INFORMATION: Each learning unit consists of:A video unit and an Assessable content.

    • Module 4: Supervised learning: regression techniques

      Module 4: Supervised learning: regression techniques

      By Rafael Alcalá Fernández, Full professor at the University of Granada, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), will explain what is Unsupervised Learning.

      INFORMATION: Each learning unit consists of:A video unit and an Assessable content

    • Modulo 5: Supervised Learning: Classification Techniques

      Modulo 5: Supervised Learning: Classification Techniques

      Alberto Fernández Hilario, Senior Professor at the University of Granada, Andalusian Interuniversity Institute on Data Science and Computational Intelligence (DasCI); Coordinator of this fifth module, he will introduce us in Classification Techniques.

      INFORMATION: Each learning unit consists of:A video unit and an Assessable content

    • Modulo 6: Unsupervised learning: Clustering and association rules

      Modulo 6: Unsupervised learning: Clustering and association rules

      Jesús Alcalá Fernández, Associate Professor at CCIA of the University of Granada and Member of the Andalusian Interuniversity Institute in Data Science and Computational Intelligence (DasCI); Coordinator of this MOOC and of this sixth module, will introduce us to Unsupervised Learning: Clustering and Association Rules.

      INFORMATION: Each learning unit consists of:A video unit and an Assessable content

    • Module 7: Big Data

      Module 7: Big Data

      In the following video, Francisco Javier García Castellano, Associate Professor of Computer Science and Artificial Intelligence at the University of Granada and coordinator of the seventh module, makes an introduction to Big Data.

      INFORMATION: Each learning unit consists of:A video unit and an Assessable content

    • Module 8: Graphic Tool: KNIME

      Module 8: Graphic Tool: KNIME

      In the following content, José Manuel Soto Hidalgo, Associate Professor at ATC, University de Granada will introduce us in KNIME

      INFORMATION: Each learning unit consists of:A video unit and an Assessable content


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