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 2 out of 5 points 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.

Learning outcomes	Study programme competences
Know the different machine learning techniques and apply them correctly.	A45	B1 B9	C2 C6 C7 C8
To be able to combine the results of different techniques.	A45	B1 B9
To be able to correctly compare the results obtained with different techniques.	A45	B1	C2
Learn and apply the methodology of using these techniques in the resolution of real problems.	A45	B1 B9	C2 C6 C7 C8

Topic	Sub-topic
Unit 1: Introducción	1.1. Introduction to Machine Learning 1.2. Introduction to Inductive Learning
Unit 2: Supervised Learning	2.1. Introduction 2.2. Support-Vector Machines 2.3. Decision Trees and Rules 2.4. Regression. Regression Trees 2.5. Bayesian Learning 2.6. Instant-Based Learning 2.7. Artificial Neural Networks
Unit 3: Unsupervised Learning	3.1. Unsupervised learning: clustering 3.2. Unsupervised neural networks
Unit 4: Reinforcement Learning	4.1. Markov Decision Processes 4.2. Reinforcement Learning
Unit 5: Other concepts	5.1. Deep Learning 5.2. Evaluation and hypotheses contrast 5.3. Metaclassifiers

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
Guest lecture / keynote speech	A45 C7 C8	21	42	63
Laboratory practice	A45 B1 B9	12	24	36
Supervised projects	A45 C2 C6	7	19	26
Objective test	A45 C7 C8	2	20	22

Personalized attention		3	0	3

(*)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 a practical problem by using the different techniques that will be explained in the theory classes.
Supervised projects	Writing, under the supervision of the teacher, of the report explaining the resolution of the problem carried out in the laboratory practices and the results obtained. This work must be presented in class.
Objective test	This is a written assessment test in which the student must demonstrate the knowledge acquired from the subject.

Methodologies	Competencies	Description	Qualification
Objective test	A45 C7 C8	Test questions about the contents of the course, based on the different computer learning techniques and their applications.	50
Supervised projects	A45 C2 C6	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
Laboratory practice	A45 B1 B9	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.	25

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 2 out of 5 points 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.

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 Ethem Alpaydin (2004). Introduction to Machine Learning. MIT Press David Aha (). Lazy Learning. Kluwer Academics Publishers T.M. Mitchell (1997). Machine Learning. McGraw Hill Richard Sutton, Andrew Barto (). Reinforcement Learning. An Introduction. MIT Press Saso Dzeroski, Nada Lavrac (). Relational Data Mining. Springer Andrew Webb (2002). Statistical Pattern Recognition. Wiley

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