XLIII. IMPLEMENTATION OF MACHINE LEARNING TECHNIQUES AT UNDERGRADUATE LEVEL
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Abstract
The ongoing COVID-19 crisis has led us to think over our teaching methods. The
undergraduate education is not apart from the current transformation, and consequently,
teachers and researchers have realized the need to apply new techniques and methodologies
in our projects. Machine Learning algorithms have become very popular in Astronomy, for
their versatility and high-power of prediction, that results from using these techniques in large
data sets. There are a few current projects that I am developing with undergraduate students:
the first one explores the potential of neural networks to improve the estimate of
cosmological parameters of the ΛCDM standard model. The second work uses the
unsupervised K-means algorithm to classify quasars (very massive, luminous and distant
objects), from their distinctive physical properties observed by SDSS (Sloan Digital Sky
Survey) in data releases 12 and 14. Based on the spectra from these powerful black holes at
high redshift, and other physical properties detected by the Sloan, we classify quasars that
present certain absorption lines.
The main objective of this work is to show the impact of implementing modern Machine
Learning techniques in research projects with Engineering students and the perspectives of
these proposals in other disciplines.
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machine learning, deep learning, higher education, neural networks.
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