In order to obtain a pass in this subject, a minimum mark of 50 must be obtained in all the above methodologies, with a minimum of 30 in the Tutored Work and 25 in the sum of the Oral Presentation and the Document Analysis.

If the student does not pass the subject at the first sitting, he/she will have to repeat the necessary activities of the methodology(ies) that were not passed at the second sitting. As an example, if a student passed the Oral Presentation + Document Analysis part, but failed the tutored assignments, he/she will have to repeat the practical assignments necessary to achieve a pass, normally those that were not passed individually.

Assessment of the extraordinary call: students who opt for this call will have to do the tutored work and oral presentation methodologies, but not the Document Analysis. The value of this methodology is added to that of Oral Presentation, becoming worth 40%. It is necessary to contact the professors at the beginning of the term (January) in order to have a sufficient deadline for delivery.

Part-time students will be able to accumulate 15% of the grade corresponding to the Document Analysis in the oral presentation in all the sessions. This modification must be requested to the lecturers of the subject at the beginning of the term. Likewise, in the event of not being able to give the oral presentation with the rest of the students, an alternative date must be agreed with the lecturers at all the sessions.

In the case of plagiarism in internships or teaching assignments, article 11, section 4 b) of the UDC Student Disciplinary Regulations will be taken into account:

b) Failure grade in the exam session in which the offence is committed and with respect to the subject in which it is committed: the student will be graded with a "fail" (numerical grade 0) in the corresponding exam session of the academic year, whether the offence is committed on the first or second occasion. To this end, the student's grade will be modified at the first opportunity, if necessary.

Learning outcomes	Study programme competences
Coñecer as principais técnicas de clasificación supervisada e non supervisada, e o seu uso práctico	AJ8	BJ1 BJ2 BJ6 BJ13	CJ1 CJ3
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)
Data processing methods	Data Preparation Dimensionality reduction
Experimental methodology and result analysis	Methods for estimating error Results analysis Model comparison
Regression methods for modeling and prediction	Introduction Main techniques Artificial Neural Networks

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
Supervised projects	B2 B3 B4 B13 C1 C3	0	37	37
Oral presentation	B1 B5 B15 B14 B6 C7 C9 C11	3	9	12
ICT practicals	A8 B13 B14 B16 B6 C11	10.5	21	31.5
Document analysis	B3 B5 B14 B6 C11	2	8	10
Guest lecture / keynote speech	B1 B6 C6 C8	16	2	18

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	In person computer 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.
Document analysis	Methodological technique that involves the use of audiovisual and/or bibliographic documents relevant to the subject matter with activities specifically designed for their analysis. In this case, it will be used in a context of "flipped classroom" in which the theoretical concepts will be reviewed by the students independently prior to the lecture session, in which an activity will be carried out to assess their understanding.
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.	25
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.	60
Document analysis	B3 B5 B14 B6 C11	Part of the lectures will be used to evaluate the understanding of the documentary sources, which will be provided by the teachers prior to the class for consultation and understanding. These evaluations will be carried out by means of group work, small reports, questionnaires, or other methodologies that allow an objective assessment of the degree of analysis carried out.	15

Assessment comments
In order to obtain a pass in this subject, a minimum mark of 50 must be obtained in all the above methodologies, with a minimum of 30 in the Tutored Work and 25 in the sum of the Oral Presentation and the Document Analysis. If the student does not pass the subject at the first sitting, he/she will have to repeat the necessary activities of the methodology(ies) that were not passed at the second sitting. As an example, if a student passed the Oral Presentation + Document Analysis part, but failed the tutored assignments, he/she will have to repeat the practical assignments necessary to achieve a pass, normally those that were not passed individually. Assessment of the extraordinary call: students who opt for this call will have to do the tutored work and oral presentation methodologies, but not the Document Analysis. The value of this methodology is added to that of Oral Presentation, becoming worth 40%. It is necessary to contact the professors at the beginning of the term (January) in order to have a sufficient deadline for delivery. Part-time students will be able to accumulate 15% of the grade corresponding to the Document Analysis in the oral presentation in all the sessions. This modification must be requested to the lecturers of the subject at the beginning of the term. Likewise, in the event of not being able to give the oral presentation with the rest of the students, an alternative date must be agreed with the lecturers at all the sessions. In the case of plagiarism in internships or teaching assignments, article 11, section 4 b) of the UDC Student Disciplinary Regulations will be taken into account: b) Failure grade in the exam session in which the offence is committed and with respect to the subject in which it is committed: the student will be graded with a "fail" (numerical grade 0) in the corresponding exam session of the academic year, whether the offence is committed on the first or second occasion. To this end, the student's grade will be modified at the first opportunity, if necessary.

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
	A Whirlwind Tour of Python by Jake VanderPlas (O’Reilly): HTML book Exercises source code
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