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

Early assessment (December): students who choose this call have to carry out Supervised Projects and Oral Presentation methodologies but not the Objective test. The value of this methodology will be added to the Supervised Projects value, which will value 70%. At the beginning of the academic period (September), the students must let the professors know they'll attend the early assessment to get enough time to accomplish the work

Students with part-time enrollment, in the case of not being able to carry out an oral presentation with the rest of the students, nor in person neither online, an alternative date must be arranged with the teachers. This modification must be requested to the subject teachers at the beginning of the course.

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
Objective test	B1 B14 B6	1	0	1
Guest lecture / keynote speech	B1 B6 C6 C8	17	10	27

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.
Objective test	A multiple-choice or test-type questionnaire that is completed online at the end of the master theory sessions, with the aim of assessing the degree of participation, attention and understanding of the concepts explained by the teacher. Tools like Moodle, Microsoft Forms or Kahoot could be used.
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.	30
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
Objective test	B1 B14 B6	Understanding the concepts explained by the teacher in the master sessions implies that students participate in the classes in an active way, raising questions and making the most of personal interaction. This understanding is valued in the final grade of the course through the online questionnaires that are made in the final minutes of each master session.	10

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 Early assessment (December): students who choose this call have to carry out Supervised Projects and Oral Presentation methodologies but not the Objective test. The value of this methodology will be added to the Supervised Projects value, which will value 70%. At the beginning of the academic period (September), the students must let the professors know they'll attend the early assessment to get enough time to accomplish the work Students with part-time enrollment, in the case of not being able to carry out an oral presentation with the rest of the students, nor in person neither online, an alternative date must be arranged with the teachers. This modification must be requested to the subject teachers at the beginning of the course.

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

Other comments
The documents to be deliver in this subject: - Virtual format or digital support will be requested. - They'll be done on the Virtual Campus without printing them. In case they’re done in paper: - Don't use plastics. - Use double-sided printing. - Use recycled paper. - Avoid printing drafts.