Educational guide
IDENTIFYING DATA							2024_25
Subject	MATHEMATICAL MODELLING II					Code	00717030
Study programme	0717 - GRADO INGENIERÍA DATOS INTELIGENCIA ARTIFICIAL
Descriptors	Credit.	Type	Year	Period
	6	Compulsory	Third	Second
Language
Prerequisites
Department	MATEMATICAS
Coordinador	CARRIEGOS VIEIRA , MIGUEL				E-mail	mcarv@unileon.es ncasg@unileon.es
Lecturers	CARRIEGOS VIEIRA , MIGUEL CASTRO GARCIA, NOEMI DE
Web	http://
General description
Tribunales de Revisión	Tribunal titular Cargo Departamento Profesor Presidente MATEMATICAS PISABARRO MANTECA , MARIA JESUS Secretario MATEMATICAS LOPEZ CABECEIRA , MONTSERRAT Vocal MATEMATICAS MUNOZ CASTANEDA , ANGEL LUIS Tribunal suplente Cargo Departamento Profesor Presidente MATEMATICAS GRANJA BARON , ANGEL Secretario MATEMATICAS MAZCUñAN NAVARRO , EVA MARIA Vocal MATEMATICAS SANTAMARIA SANCHEZ , RAFAEL

Competencias

Code
A18963
B5800
B5802
B5806
B5807
B5808
C2	CMECES2 That students know how to apply their knowledge to their work or vocation in a professional manner and possess the skills that are usually demonstrated through the development and defense of arguments and the resolution of problems within their area of study.
C5	CMECES5 That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy

Learning aims

	Competences
	A18963	B5800 B5802 B5806 B5807 B5808
	A18963	B5800 B5802 B5806 B5807 B5808
	A18963	B5800 B5802 B5806 B5807 B5808	C2 C5
	A18963	B5800 B5802 B5806 B5807 B5808	C2 C5
	A18963	B5800 B5802 B5806 B5807 B5808	C2 C5
	A18963	B5800 B5802 B5806 B5807 B5808	C2

Contents

Topic	Sub-topic

Planning

Methodologies  ::  Tests
	Class hours	Hours outside the classroom	Total hours
Problem solving, classroom exercises	20	20	40
Lecture	20	20	40
Extended-answer tests	10	20	30
Practical tests	10	30	40
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies

Methodologies   ::  

	Description
Problem solving, classroom exercises
Lecture

Personalized attention

Lecture Problem solving, classroom exercises Extended-answer tests Practical tests

Description

Assessment

	Description	Qualification
Extended-answer tests		al menos el 50%
Practical tests		al menos el 10%
Other comments and second call

Sources of information

Access to Recommended Bibliography in the Catalog ULE

Basic	J. Gallier & J. Quaintance, Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning, Penn Univ., 2020 S.R. Das, ata science: theories, models, algorithms, and analytics, , 2016 S.L. Brunton, J.N. Kutz, Data-Driven Science and Engineering, , 2019 G. Strang, Linear Algebra and learning from data, , 2019
Complementary	A.S. Bandeira, 10 lectures and 42 open problems in Mathematics of Data Science, , 2015 P.H. Winston, Artificial intelligence, , 1993 J.M. Phillips, Mathematical foundations for data analysis, , 2018 M.P. Deisenroth, A. Aldo Faisal, C. Soon Ong, Mathematics for machine learning, , 2020

Recommendations

Subjects that it is recommended to have taken before

ANÁLISIS MATEMÁTICO I / 00717001 MATEMÁTICA FINITA I / 00717002 ANÁLISIS MATEMÁTICO II / 00717006 ÁLGEBRA LINEAL I / 00717007 LINEAR ALGEBRA II / 00717011 PROBABILITY CALCULUS / 00717012 MATHEMATICAL MODELLING I / 00717013 FINITE MATHEMATICS II / 00717016