Educational guide

IDENTIFYING DATA

2024_25

Subject

MACHINE LEARNING

Code

00717014

Study programme

0717 - GRADO INGENIERÍA DATOS INTELIGENCIA ARTIFICIAL

Descriptors

Credit.

Type

Year

Period

Compulsory

Second

First

Language

Castellano

Prerequisites

Department

ING.ELECTR.DE SIST. Y AUTOMATI

Coordinador

ALAIZ RODRÍGUEZ , ROCÍO

E-mail

ralar@unileon.es
mgaro@unileon.es
fjaÑm@unileon.es

Lecturers

ALAIZ RODRÍGUEZ , ROCÍO

GARCIA ORDAS , MARIA TERESA

JAÑEZ MARTINO , FRANCISCO

Web

http:// http://agora.unileon.es

General description

Tribunales de Revisión

Tribunal titular
Cargo	Departamento	Profesor
Presidente	ING.ELECTR.DE SIST. Y AUTOMATI	BENAVIDES CUELLAR , MARIA DEL CARMEN
Secretario	ING.ELECTR.DE SIST. Y AUTOMATI	BENITEZ ANDRADES , JOSE ALBERTO
Vocal	ING.ELECTR.DE SIST. Y AUTOMATI	BLAZQUEZ QUINTANA , LUIS FELIPE

Tribunal suplente
Cargo	Departamento	Profesor
Presidente	ING.ELECTR.DE SIST. Y AUTOMATI	GARCIA RODRIGUEZ , ISAIAS
Secretario	ING.ELECTR.DE SIST. Y AUTOMATI	ALAIZ MORETON , HECTOR
Vocal	ING.ELECTR.DE SIST. Y AUTOMATI	ALONSO CASTRO , SERAFIN

Competencias

Code
A18964
A18966
A18973
A18983
B5801
B5802
B5806
B5807
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.
C4	CMECES4 That students can transmit information, ideas, problems and solutions to both a specialised and non-specialised audience

Learning aims

	Competences
Understand and apply the basic algorithmic procedures of computational technologies to design solutions to problems, analysing the suitability and complexity of proposed algorithms. Apply the fundamental principles, methods and basic techniques of data science and artificial intelligence for their practical application to a specific problem or domain, interpreting the results appropriately. Understand, select and apply the most appropriate automatic learning techniques and tools to transform data into knowledge to solve a problem. Understand how to design solutions based on artificial neural networks to solve problems in the field of data engineering.	A18964 A18966 A18973 A18983	B5801 B5802 B5806 B5807	C2 C4

Contents

Topic

Sub-topic

Block I: Machine Learning

INTRODUCTION
Introduction to Artificial Intelligence. Introduction to machine learning: fundamentals and types of machine learning. Domains and application examples.

METHODOLOGY
Data collection. Choice of features. Model selection. Classifier Tuning/Training, Evaluation. Naive Bayes classifier.

NON-PARAMETRIC SUPERVISED CLASSIFICATION TECHNIQUES.
Fundamentals. Neighbourhood classifiers.

PARAMETRIC SUPERVISED LEARNING TECHNIQUES.
Linear and logistic regression.

DATA PROCESSING
Outlier detection. Missing samples. Data normalisation. Feature selection and extraction.

MODEL EVALUATION, VALIDATION AND SELECTION
Estimation techniques. Classification performance metrics. Other metrics. Model comparison.

ARTIFICIAL NEURAL NETWORKS.
Introduction. Architecture. The perceptron. The Adaline network. Multilayer perceptron (MLP).

UNSUPERVISED LEARNING.
Introduction. Space, distance and similarity. Partitioning and agglomerative techniques. Centroid method (K-means).

OTHER LEARNING MODELS

Planning

Methodologies :: Tests
	Class hours	Hours outside the classroom	Total hours
Problem solving, classroom exercises	5	9	14

Personal tuition	2	0	2
Assignments	3	16	19
Practicals using information and communication technologies (ICTs) in computer rooms	26	26	52

Lecture	21	27	48

Mixed tests	2	10	12
	1	2	3

(*)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
Personal tuition
Assignments
Practicals using information and communication technologies (ICTs) in computer rooms
Lecture

Personalized attention

Personal tuition

Description

These sessions will be planned to follow up on the work proposed in the course.
Other sessions to solve specific doubts about the concepts seen in the course will be requested by email and will be held in person or online.

Assessment

	Description	Qualification
Practicals using information and communication technologies (ICTs) in computer rooms		30%
Assignments		15%
Lecture		55%

Other comments and second call

Sources of information

Access to Recommended Bibliography in the Catalog ULE

Basic
	Pattern Classification, 2nd Edition. Richard O. Duda, Peter E. Hart, David G. Stork. New York: John Wiley & Sons, 2001. Grokking Machine Learning, Luis Serrano, Manning Ed, 2022. Aprendizaje Automático: Conceptos Básicos y Avanzados. Basilio Sierra Araujo (coordinador). Prentice Hall. 2007. Aprende Machine Learning en Español: Teoría + práctica. Juan Ignacio Bagnato. ISBN:978-84-09-25816-1. 2020.
Complementary
	Introduction to Machine Learning with Python. A guide for data scientists. Andreas C. Müller and Sarah Guido. O’Reilly, 2016. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. Géron, A. O'Reilly Media, Inc., 2022 Introduction to Machine Learning. 2nd Edition. Ethem Alpaydin. The MIT Press, 2010. MachineLearning.T. M. Mitchell,. New York: McGraw Hill. 1997. Combining Pattern Classifiers: Methods and Algorithms, L. Kuncheva, Wiley, Second Edition, 2014.

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