In order to pass the subject, the student must obtain a minimumscore of 5 out of 10 in the result of combining the grades of the objectivetest, the laboratory practices and the supervised works. In addition, thestudent must obtain a minimum score of 2 out of 5 points in the objective test.If the student does not obtain this minimum grade, the grade of the subjectwill 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 workduring the credits of practice of the subject. The student can retake the examination of the objective test, the criteria for obtaining the total scorebeing 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.

No-show qualification:

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

Fraudulent performance of exercises or tests:

The fraudulent execution of tests or assessment activities, once proven, will result in a direct failing grade in the examination in which it was committed. The student will be given a grade of "suspenso" (numeric grade 0) in the corresponding academic year's examination, whether the offense occurs in the first opportunity or the second. In order to do so, the student's grade will be modified in the first opportunity's record, if necessary.

Learning outcomes	Study programme competences
Know, understand and know how to use the fundamentals of machine learning processes.	A1 A12	B5 B10	C3
Know the fundamentals of regression, classification and clustering models.	A2 A12	B3 B7 B9
Know how to build advanced statistical models for data analysis.	A2 A12 A15	B7 B9	C3
Know how to base modeling and problem solving using machine learning techniques.	A1 A2 A12	B5 B7 B9	C3
Know how to build machine learning models for regression, classification and clustering.	A1 A2 A12 A15	B5 B7 B9 B10	C3

Topic	Sub-topic
1. Introduction	1.1. Introduction to Machine Learning 1.2. Learning Paradigms 1.3. Inductive Learning 1.4. No free Lunch Theorems
2. Supervised learning	2.1. Introduction 2.2. Artificial Neural Networks 2.3. Support Vector Machines 2.4. Decision trees 2.5. Regression trees and regression model trees 2.6. Instance-based learning
4. Methodologies in data analysis	4.1. Training, evaluation and model selection methodologies 4.2. Methodologies of a data analysis project
3. Evolutionary Computation	3.1. Genetic Algorithms 3.2. Genetic Programming 3.3. Swarms and other Evolutionary Computation techniques
5. Clustering	5.1. Clustering methods

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
Guest lecture / keynote speech	A12 A15 B5 B9 B10 C3	30	38	68
Laboratory practice	A1 A2 B3 B7 C3	15	24	39
Supervised projects	A1 A2 A15 B3 B7 B10	15	24	39
Objective test	A1 A12 B5 B7 B10	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	Description
Guest lecture / keynote speech	Theoretical teaching of the subject matter of the course
Laboratory practice	Solve practical problems by using the different techniques that will be explained in the theory classes.
Supervised projects	Writing, under the supervision of the teacher, of the reports explaining the resolution of the problems carried out in the laboratory practices and the results obtained.
Objective test	This is a written assessment test in which the student must demonstrate the knowledge acquired from the subject.

Methodologies	Competencies	Description	Qualification
Laboratory practice	A1 A2 B3 B7 C3	Development of a Machine Learning system based on explanations made in theory.	25
Supervised projects	A1 A2 A15 B3 B7 B10	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	A1 A12 B5 B7 B10	Test questions about the contents of the course, based on the different machine learning techniques and their applications.	50

Assessment comments
In order to pass the subject, the student must obtain a minimumscore of 5 out of 10 in the result of combining the grades of the objectivetest, the laboratory practices and the supervised works. In addition, thestudent must obtain a minimum score of 2 out of 5 points in the objective test.If the student does not obtain this minimum grade, the grade of the subjectwill 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 workduring the credits of practice of the subject. The student can retake the examination of the objective test, the criteria for obtaining the total scorebeing 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. No-show qualification: The student will receive the qualification of "no-show" when he/she does not take the final exam. Fraudulent performance of exercises or tests: The fraudulent execution of tests or assessment activities, once proven, will result in a direct failing grade in the examination in which it was committed. The student will be given a grade of "suspenso" (numeric grade 0) in the corresponding academic year's examination, whether the offense occurs in the first opportunity or the second. In order to do so, the student's grade will be modified in the first opportunity's record, if necessary.

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

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