A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling

Autores
Lafuente, Deborah; Cohen, Brenda; Fiorini, Guillermo; Garcia, Agustin Alejo; Bringas, Mauro; Morzan, Ezequiel Martin; Onna, Diego Ariel
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks and assignments. Python, one of the most popular programming languages, is open source, free to use, and has plenty of learning resources. The workshop is designed for students without previous experience in programming, and it aims for a deeper understanding of the complexity of concepts in programming and machine learning. The examples used correspond to real data from physicochemical characterizations of wine, a content that is of interest for students. The contents of the workshop are introduction to Python, basic statistics, data visualization, and dimension reduction, classification, and regression.
Fil: Lafuente, Deborah. Universidad de Buenos Aires; Argentina
Fil: Cohen, Brenda. Universidad de Buenos Aires; Argentina
Fil: Fiorini, Guillermo. Universidad de Buenos Aires; Argentina
Fil: Garcia, Agustin Alejo. Universidad de Buenos Aires; Argentina
Fil: Bringas, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
Fil: Morzan, Ezequiel Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
Fil: Onna, Diego Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
Materia
CHEMOINFORMATICS
CHEMOMETRICS
COMPUTATIONAL CHEMISTRY
COMPUTER-BASED LEARNING
INTERDISCIPLINARY/MULTIDISCIPLINARY
UPPER-DIVISION UNDERGRADUATE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/147938
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and ModelingLafuente, DeborahCohen, BrendaFiorini, GuillermoGarcia, Agustin AlejoBringas, MauroMorzan, Ezequiel MartinOnna, Diego ArielCHEMOINFORMATICSCHEMOMETRICSCOMPUTATIONAL CHEMISTRYCOMPUTER-BASED LEARNINGINTERDISCIPLINARY/MULTIDISCIPLINARYUPPER-DIVISION UNDERGRADUATEhttps://purl.org/becyt/ford/5.3https://purl.org/becyt/ford/5Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks and assignments. Python, one of the most popular programming languages, is open source, free to use, and has plenty of learning resources. The workshop is designed for students without previous experience in programming, and it aims for a deeper understanding of the complexity of concepts in programming and machine learning. The examples used correspond to real data from physicochemical characterizations of wine, a content that is of interest for students. The contents of the workshop are introduction to Python, basic statistics, data visualization, and dimension reduction, classification, and regression.Fil: Lafuente, Deborah. Universidad de Buenos Aires; ArgentinaFil: Cohen, Brenda. Universidad de Buenos Aires; ArgentinaFil: Fiorini, Guillermo. Universidad de Buenos Aires; ArgentinaFil: Garcia, Agustin Alejo. Universidad de Buenos Aires; ArgentinaFil: Bringas, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Morzan, Ezequiel Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Onna, Diego Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaAmerican Chemical Society2021-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/147938Lafuente, Deborah; Cohen, Brenda; Fiorini, Guillermo; Garcia, Agustin Alejo; Bringas, Mauro; et al.; A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling; American Chemical Society; Journal Of Chemical Education; 98; 9; 8-2021; 2892-28980021-9584CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acs.jchemed.1c00142info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.jchemed.1c00142info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:40:31Zoai:ri.conicet.gov.ar:11336/147938instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:40:31.995CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling
title A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling
spellingShingle A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling
Lafuente, Deborah
CHEMOINFORMATICS
CHEMOMETRICS
COMPUTATIONAL CHEMISTRY
COMPUTER-BASED LEARNING
INTERDISCIPLINARY/MULTIDISCIPLINARY
UPPER-DIVISION UNDERGRADUATE
title_short A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling
title_full A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling
title_fullStr A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling
title_full_unstemmed A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling
title_sort A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling
dc.creator.none.fl_str_mv Lafuente, Deborah
Cohen, Brenda
Fiorini, Guillermo
Garcia, Agustin Alejo
Bringas, Mauro
Morzan, Ezequiel Martin
Onna, Diego Ariel
author Lafuente, Deborah
author_facet Lafuente, Deborah
Cohen, Brenda
Fiorini, Guillermo
Garcia, Agustin Alejo
Bringas, Mauro
Morzan, Ezequiel Martin
Onna, Diego Ariel
author_role author
author2 Cohen, Brenda
Fiorini, Guillermo
Garcia, Agustin Alejo
Bringas, Mauro
Morzan, Ezequiel Martin
Onna, Diego Ariel
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv CHEMOINFORMATICS
CHEMOMETRICS
COMPUTATIONAL CHEMISTRY
COMPUTER-BASED LEARNING
INTERDISCIPLINARY/MULTIDISCIPLINARY
UPPER-DIVISION UNDERGRADUATE
topic CHEMOINFORMATICS
CHEMOMETRICS
COMPUTATIONAL CHEMISTRY
COMPUTER-BASED LEARNING
INTERDISCIPLINARY/MULTIDISCIPLINARY
UPPER-DIVISION UNDERGRADUATE
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.3
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks and assignments. Python, one of the most popular programming languages, is open source, free to use, and has plenty of learning resources. The workshop is designed for students without previous experience in programming, and it aims for a deeper understanding of the complexity of concepts in programming and machine learning. The examples used correspond to real data from physicochemical characterizations of wine, a content that is of interest for students. The contents of the workshop are introduction to Python, basic statistics, data visualization, and dimension reduction, classification, and regression.
Fil: Lafuente, Deborah. Universidad de Buenos Aires; Argentina
Fil: Cohen, Brenda. Universidad de Buenos Aires; Argentina
Fil: Fiorini, Guillermo. Universidad de Buenos Aires; Argentina
Fil: Garcia, Agustin Alejo. Universidad de Buenos Aires; Argentina
Fil: Bringas, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
Fil: Morzan, Ezequiel Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
Fil: Onna, Diego Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
description Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks and assignments. Python, one of the most popular programming languages, is open source, free to use, and has plenty of learning resources. The workshop is designed for students without previous experience in programming, and it aims for a deeper understanding of the complexity of concepts in programming and machine learning. The examples used correspond to real data from physicochemical characterizations of wine, a content that is of interest for students. The contents of the workshop are introduction to Python, basic statistics, data visualization, and dimension reduction, classification, and regression.
publishDate 2021
dc.date.none.fl_str_mv 2021-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/147938
Lafuente, Deborah; Cohen, Brenda; Fiorini, Guillermo; Garcia, Agustin Alejo; Bringas, Mauro; et al.; A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling; American Chemical Society; Journal Of Chemical Education; 98; 9; 8-2021; 2892-2898
0021-9584
CONICET Digital
CONICET
url http://hdl.handle.net/11336/147938
identifier_str_mv Lafuente, Deborah; Cohen, Brenda; Fiorini, Guillermo; Garcia, Agustin Alejo; Bringas, Mauro; et al.; A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling; American Chemical Society; Journal Of Chemical Education; 98; 9; 8-2021; 2892-2898
0021-9584
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acs.jchemed.1c00142
info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.jchemed.1c00142
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613282352070656
score 13.070432

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