The evaluation of this subject is based on the pass of the two main methodologies, Supervised Projects and Oral Presentation, in an independent way. The first is focused on the practical demonstration of the knowledge and skills acquired to solve engineering problems through automatic learning techniques, and the second on the realization and exposition of a work on a specific topic within the theoretical topics. Thus, in case the student does not pass the subject in the ordinary call, he/she will have to repeat the necessary activities of the method(s) that were not passed in the extraordinary call. As an example, if a student passed the Oral Presentation but failed in the supervised projects, he/she will have to repeat the projects necessary to reach the passing grade, normally that/those that individually were not passed

Students with part-time enrollment may accumulate 10% of the grade corresponding to class attendance in the other activities, both in theory and in practice in the case of not being able to attend classes regularly in person. This modification must be requested to the subject teachers at the beginning of the course. Likewise, in the case of not being able to carry out an oral presentation with the rest of the students, an alternative date must be arranged with the teachers.

Learning outcomes	Study programme competences
Develop an autonomous control system for its operation in a real environment	AJ8	BJ1 BJ4 BJ6 BJ13 BJ14	CJ1 CJ3 CJ11
Know the non-resolved problems in autonomous robotics	AJ8	BJ1 BJ4 BJ6 BJ13 BJ14	CJ1 CJ3 CJ11
Know the problems of sensing and actuation in systems that operate in the real world and real time	AJ8	BJ1 BJ4 BJ6 BJ13 BJ14	CJ1 CJ3 CJ11
Know the problems of knowledge representation in autonomous robotics		BJ1 BJ4 BJ5 BJ6 BJ14 BJ16	CJ1 CJ6 CJ7 CJ8
Know the problems to tackle when an autonomous robotic control system is developed		BJ1 BJ2 BJ3 BJ13 BJ14 BJ15	CJ3 CJ6 CJ7 CJ8 CJ9 CJ11

Topic	Sub-topic
Introduction	Preliminary concepts. Types of problems: classification, regression, clustering, anomaly detection, etc. Forms of learning: supervised, unsupervised, reinforcement, etc.
Classification and clustering methods	Introduction Supervised classification algorithms Unsupervised classification algorithms (clustering)
Regression methods for modeling and prediction	Introduction Mian techniques
Data processing methods	Data Preparation Dimensionality reduction
Experimental methodology and result analysis	Metrics for model evaluation Methods for estimating error Graphical methods for comparing results
Optimization and search	Heuristic and metaheuristic methods Evolutionary algorithms

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
Supervised projects	B2 B3 B4 B13 C1 C3	0	30	30
Oral presentation	B1 B5 B15 B14 B6 C7 C9 C11	2	10	12
ICT practicals	A8 B13 B14 B16 B6 C11	18	36	54
Guest lecture / keynote speech	B1 B6 C8 C6	10	2.5	12.5

Personalized attention		4	0	4

(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies	Description
Supervised projects	Programming exercises in which some of the techniques seen in the theory classes will be implemented on real engineering problems, using the programming language selected by the teachers. These exercises will be carried out by the students autonomously and their progress will be tutored by the teachers.
Oral presentation	Theoretical work or works about a specific topic from the contents that will be orally presented and discussed with other students
ICT practicals	Computer classroom sessions in which teachers explain the use and programming of automatic learning techniques as seen in theory, so that students acquire sufficient skills to use them autonomously.
Guest lecture / keynote speech	Oral exposition by the teachers of the theory of the subject.

Methodologies	Competencies	Description	Qualification
Oral presentation	B1 B5 B15 B14 B6 C7 C9 C11	The oral presentation, the participation in the discussion and the written inform will be considered in the final qualification. It is mandatory to pass this methodology independently in order to pass the whole subject.	40
ICT practicals	A8 B13 B14 B16 B6 C11	The attendance to the ICT practical classes will be considered in the final mark	5
Guest lecture / keynote speech	B1 B6 C8 C6	The attendance to the keynote speeches will be considered in the final mark	5
Supervised projects	B2 B3 B4 B13 C1 C3	Different programming projects will be proposed along the course that must be carried out in an autonomous way by the student and that will be presented and explained to the teachers afterwards. It is mandatory to pass this methodology independently in order to pass the whole subject.	50

Assessment comments
The evaluation of this subject is based on the pass of the two main methodologies, Supervised Projects and Oral Presentation, in an independent way. The first is focused on the practical demonstration of the knowledge and skills acquired to solve engineering problems through automatic learning techniques, and the second on the realization and exposition of a work on a specific topic within the theoretical topics. Thus, in case the student does not pass the subject in the ordinary call, he/she will have to repeat the necessary activities of the method(s) that were not passed in the extraordinary call. As an example, if a student passed the Oral Presentation but failed in the supervised projects, he/she will have to repeat the projects necessary to reach the passing grade, normally that/those that individually were not passed Students with part-time enrollment may accumulate 10% of the grade corresponding to class attendance in the other activities, both in theory and in practice in the case of not being able to attend classes regularly in person. This modification must be requested to the subject teachers at the beginning of the course. Likewise, in the case of not being able to carry out an oral presentation with the rest of the students, an alternative date must be arranged with the teachers.

Basic	Gonzalo Pajares Martínez, Jose Manuel de la Cruz García (2010). Aprendizaje automático : un enfoque práctico. Ra-Ma Ethem Alpaydin (2014). Introduction to Machine Learning. MIT Press Marsland, Stephen (2014). Machine Learning: An Algorithmic Perspective. Chapman and Hall/CRC Press Christopher M. Bishop (2010). Pattern Recognition and Machine Learning. Springer

Complementary	Aurelien Geron (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media Andreas C. Müller, Sarah Guido (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media Kevin P. Murphy (2010). Machine Learning, a probabilistic perspective. MIT Press Sebastian Raschka, Vahid Mirjalili (2019). Python machine learning : aprendizaje automático y aprendizaje profundo con Python, scikit-learn y TensorFlow. Marcombo

Other comments
A entrega dos traballos documentais que se realicen nesta materia: - Solicitarase en formato virtual e/ou soporte informático. - Realizarase a través de Moodle, en formato dixital sen necesidade de imprimilos De se realizar en papel: - Non se empregarán plásticos. - Realizaranse impresións a dobre cara.  - Empregarase papel reciclado. - Evitarase a impresión de borradores.