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How to Improve the Teaching of Computational Machine Learning Applied to Large-Scale Data Science: The Case of Public Universities in Mexico

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Intelligent Systems and Applications (IntelliSys 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 544))

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Abstract

Teaching along with training on Machine Learning (ML) and Big Data in Mexican universities has become a necessity that requires the application of courses, handbooks, and practices that allow improvement in the learning of Data Science (DS) and Artificial Intelligence (AI) subjects. This work shows how the academy and the Information Technology industry use tools to analyze large volumes of data to support decision-making, which is hard to treat and interpret directly. A solution to some large-scale national problems is the inclusion of these subjects in related courses within specialization areas that universities offer. The methodology in this work is as follows: 1) Selection of topics and tools for ML and Big Data teaching, 2) Design of practices with application to real data problems, and 3) Implementation and/or application of these practices in a specialization diploma. Results of a survey applied to academic staff and students are shown. The survey respondents have already taken related courses along with those specific topics that the proposed courses and practices will seek to strengthen, developing needed skills for solving problems where ML/DL and Big Data are an outstanding alternative of solution.

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Acknowledgment

We are grateful for the support of the Instituto de Investigaciones en Ecosistemas y Sustentabilidad (IIES), the CA TRATEC - PRODEP of the Universidad Tecnológica de Morelia (UTM), the TecNM campus Morelia, the Escuela Nacional de Estudios Superiores (ENES), UNAM Campus Morelia, and DGAPA UNAM PAPIME PE106021. Especially thanks to MGTI. Atzimba G. López M., MTI. Alberto Valencia G., Eng. Oscar Álvarez, MTI. Pablo García C. and Eng. Javier Huerta S., for their technical support, comments, and analysis of the statistical calculations. We thank Eng. Alfredo A. Aviña, for his help in applying the survey and Web page support.

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Authors and Affiliations

  1. Escuela Nacional de Estudios Superiores Unidad Morelia, UNAM Campus Morelia, 58190, Morelia, Michoacán, Mexico

    Sergio Rogelio Tinoco-Martínez, Froylan Hernández-Rendón, Mariana Michell Flores-Monroy & Bruce Hiram Ginori-Rodríguez

  2. Instituto de Investigaciones en Ecosistemas y Sustentabilidad, UNAM Campus Morelia, 58190, Morelia, Michoacán, Mexico

    Heberto Ferreira-Medina

  3. Tecnológico Nacional de México, Campus Morelia, DSC, Morelia, Michoacán, Mexico

    Heberto Ferreira-Medina

  4. Departamento de TI, Universidad Tecnológica de Morelia. Cuerpo académico TRATEC-PRODEP. Morelia, 58200, Morelia, Michoacán, México

    José Luis Cendejas-Valdez

Authors
  1. Sergio Rogelio Tinoco-Martínez
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  2. Heberto Ferreira-Medina
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  3. José Luis Cendejas-Valdez
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  4. Froylan Hernández-Rendón
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  5. Mariana Michell Flores-Monroy
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  6. Bruce Hiram Ginori-Rodríguez
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Corresponding authors

Correspondence to Heberto Ferreira-Medina or José Luis Cendejas-Valdez .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

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Tinoco-Martínez, S.R., Ferreira-Medina, H., Cendejas-Valdez, J.L., Hernández-Rendón, F., Flores-Monroy, M.M., Ginori-Rodríguez, B.H. (2023). How to Improve the Teaching of Computational Machine Learning Applied to Large-Scale Data Science: The Case of Public Universities in Mexico. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_1

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