Identifying Data 2024/25
Subject (*) Machine Learning I Code 614G02019
Study programme
Grao en Ciencia e Enxeñaría de Datos
Descriptors Cycle Period Year Type Credits
Graduate 2nd four-month period
 Second Obligatory 6
Language
Spanish
Teaching method Face-to-face
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Coordinador
Rivero Cebrián, Daniel
 E-mail
daniel.rivero@udc.es
Lecturers
Porto Pazos, Ana Belen
Rabuñal Dopico, Juan Ramon
Rivero Cebrián, Daniel
Rodríguez Tajes, Álvaro
 E-mail
ana.portop@udc.es
juan.rabunal@udc.es
daniel.rivero@udc.es
a.tajes@udc.es
Web
General description Esta asignatura presenta unha visión global da aprendizaxe automática. No temario explícanse as distintas técnicas e métodos, incluíndo aprendizaxe supervisado e no supervisado. Na parte práctica realizarase a resolución de casos reais.

Competencies / Study results
Code Study programme competences / results
A24 CE24 - Comprensión e dominio dás principais técnicas básicas e avanzadas de aprendizaxe automática, incluíndo as dedicadas ao tratamento de grandes volumes de datos.
A25 CE25 - Capacidade para identificar a adecuación de cada unha das técnicas de aprendizaxe automática á resolución dun problema, incluíndo os aspectos relacionados coa súa complexidade computacional ou a súa capacidade explicativa, de acordo aos requisitos establecidos.
A26 CE26 - Coñecemento das ferramentas informáticas actuais no campo da aprendizaxe automática, e capacidade para seleccionar a máis adecuada para a resolución dun problema.
B2 CB2 - Que os estudantes saiban aplicar os seus coñecementos ao seu traballo ou vocación dunha forma profesional e posúan as competencias que adoitan demostrarse por medio da elaboración e defensa de argumentos e a resolución de problemas dentro da súa área de estudo
B3 CB3 - Que os estudantes teñan a capacidade de reunir e interpretar datos relevantes (normalmente dentro da súa área de estudo) para emitir xuízos que inclúan unha reflexión sobre temas relevantes de índole social, científica ou ética
B7 CG2 - Elaborar adecuadamente e con certa orixinalidade composicións escritas ou argumentos motivados, redactar plans, proxectos de traballo, artigos científicos e formular hipóteses razoables.
B8 CG3 - Ser capaz de manter e estender formulacións teóricas fundadas para permitir a introdución e explotación de tecnoloxías novas e avanzadas no campo.
B9 CG4 - Capacidade para abordar con éxito todas as etapas dun proxecto de datos: exploración previa dos datos, preprocesado, análise, visualización e comunicación de resultados.
B10 CG5 - Ser capaz de traballar en equipo, especialmente de carácter multidisciplinar, e ser hábiles na xestión do tempo, persoas e toma de decisións.
C1 CT1 - Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida.

Learning aims
Learning outcomes Study programme competences / results
Understand the relationship between the complexity of learning models, training data features and overfitting, and know the mechanisms to avoid it. A24
A25
Develop skills to design the stages of a complete data analysis process based on automatic learning techniques. B2
B7
B9
B10
 C1
Know how to correctly apply automatic learning techniques to obtain reliable and significant results. A24
 B3
B8
Know the most representative and current techniques of unsupervised, semi-supervised and supervised learning. A24
 B8
Know the most representative learning techniques for the classic problems of classification, regression and clustering, and other less classic ones such as sorting problems, one class problems or multitasking. A24
 B8
Identify appropriate data analysis techniques according to the problem. A25
 B3
B8
Manage the most current tools and work environments in the field of machine learning. A26
 B2
B10

Contents
Topic Sub-topic
Introduction Introduction to Machine Learning
Learning Paradigms
Inductive Learning
No Free Lunch Theorems
Supervised learning Introduction
Artificial Neural Networks
Logistic Regression
Support Vector Machines
Decision Trees
Instance-based learning
ML Models for Regression
Evolutionary Computation Genetic Algorithms
Genetic Programming
Swarms and other Evolutionary Computation techniques
Methodologies in data analysis Training, evaluation and model selection methodologies
Methodologies of a data analysis project
Unsupervised learning Clustering methods
Self-organised networks

Planning
Methodologies / tests Competencies / Results Teaching hours (in-person & virtual) Student’s personal work hours Total hours
Guest lecture / keynote speech A24 A25 B3 B8 B9 30 38 68
Laboratory practice A26 B2 B3 B10 C1 15 24 39
Supervised projects B2 B3 B7 B9 B10 15 24 39
Objective test A24 A25 B8 B9 2 0 2
 
Personalized attention 2 0 2
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Guest lecture / keynote speech Theoretical teaching of the subject matter of the course
Laboratory practice Development of code related to the content of the subject
Supervised projects Solving a real-world problema and writing of the report explaining the resolution of this problem
Objective test Written assessment test in which the student must demonstrate the knowledge acquired from the subject.

Personalized attention
Methodologies
Laboratory practice
Supervised projects
Description
Practical work carried out with the advice of the teacher.
Writing of the explanatory report under the teacher's supervision.

Assessment
Methodologies Competencies / Results Description Qualification
Laboratory practice A26 B2 B3 B10 C1 Development of code related to the content of the subject. 25
Supervised projects B2 B3 B7 B9 B10 Resolution of a real world problem using the methodology, for which several techniques explained in theory will be used, and the student will be stimulated to generate new ideas for the resolution of this problem. Writing of the report on the resolution of the real problem carried out in the laboratory practices. The writing of the report will include a bibliographic review of the most important works related, written in English for the most part, documentation on the problem to be solved, methodology used, and comparison of the results found in the application of the different techniques, as well as a critical evaluation of both the results obtained and the information used. 25
Objective test A24 A25 B8 B9 Test questions about the contents of the course. 50
 
Assessment comments

In order to pass the subject, the student must obtain a minimum score of 5 out of 10 in the result of combining the grades of the objective test, the laboratory practices and the supervised works. In addition, the student must obtain a minimum score of 4.5 points out of 10 (2.25 points out of 5) in the objective test. If the student does not obtain this minimum grade, the grade of the subject will be that corresponding to the grade of the objective test.

In the second opportunity, the grade obtained in the laboratory practices and supervised works will be maintained, not being able to obtain again a grade since it results from the continuous evaluation of the work during the credits of practice of the subject. The student can retake the examination of the objective test, the criteria for obtaining the total score being those indicated at the beginning of this section.

Part-time students must deliver in their reports on the same dates as full-time students, and attend the RGTs in which they will be corrected. Similarly, it is recommended that they attend the practice classes.

The student will receive the qualification of "no-show" when he/she does not take the final exam.

All aspects related to "academic exemption", "study dedication," "continuity," and "academic fraud" will be governed in accordance with the current academic regulations of the UDC.

Sources of information
Basic D. Borrajo, J. González, P. Isasi (2006). Aprendizaje automático. Sanz y Torres
Basilio Sierra Araujo (2006). Aprendizaje automático: conceptos básicos y avanzados. Aspectos prácticos utilizando el software WEKA. Pearson Education
Marcos Gestal, Daniel Rivero, Juan Ramón Rabuñal, Julián Dorado, Alejandro Pazos (2010). Introducción a los Algoritmos Genéticos y a la Programación Genética. Servicio de Publicaciones de la Universidade da Coruña
Ethem Alpaydin (2004). Introduction to Machine Learning. MIT Press
David Aha (). Lazy Learning. Kluwer Academics Publishers
T.M. Mitchell (1997). Machine Learning. McGraw Hill
Saso Dzeroski, Nada Lavrac (). Relational Data Mining. Springer
Andrew Webb (2002). Statistical Pattern Recognition. Wiley

Complementary


Recommendations
Subjects that it is recommended to have taken before
Design and Analysis of Algorithms/614G02011
Regression Models/614G02012
Statistical Modeling of High Dimensional Data/614G02013
Signals and Systems/614G02014
Fundamentals of Programming II/614G02009
Fundamentals of Programming I/614G02004
Statistical Inference/614G02007

Subjects that are recommended to be taken simultaneously
Information Theory/614G02018
Mathematical Optimisation/614G02020

Subjects that continue the syllabus
Large Scale Machine Learning/614G02032
Numerical Methods for Data Science/614G02033
Machine Learning III/614G02026
Image, Video and Audio Processing/614G02028
Machine Learning II/614G02021

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.