| Competencies / Study results |
| Code | Study programme competences / results |
| A8 | ETI8 - Ability to design and project automated production systems and advanced process control. |
| B1 | CB6 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context. |
| B2 | CB7 - That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of ??study. |
| B3 | CB8 - That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments. |
| B4 | CB9 - That the students know how to communicate their conclusions -and the knowledge and ultimate reasons that sustain them- to specialized and non-specialized audiences in a clear and unambiguous way. |
| B5 | CB10 - That students have the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous. |
| B6 | G1 - Have adequate knowledge of the scientific and technological aspects in Industrial Engineering. |
| B13 | G8 - Apply the knowledge acquired and solve problems in new or unfamiliar environments within broader and multidisciplinary contexts. |
| B14 | G9 - Be able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments. |
| B15 | G10 - Knowing how to communicate the conclusions -and the knowledge and ultimate reasons that sustain them- to specialized and non-specialized publics in a clear and unambiguous way. |
| B16 | G11 - Possess the learning skills that allow to continue studying in a self-directed or autonomous way. |
| C1 | ABET (a) - An ability to apply knowledge of mathematics, science, and engineering. |
| C3 | ABET (c) - An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability. |
| C6 | ABET (f) - An understanding of professional and ethical responsibility. |
| C7 | ABET (g) - An ability to communicate effectively. |
| C8 | ABET (h) - The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context. |
| C9 | ABET (i) - A recognition of the need for, and an ability to engage in life-long learning. |
| C11 | ABET (k) - An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. |
| Learning aims | |||
| Learning outcomes | Study programme competences / results | ||
| 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 |
|
| Contents |
| 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 |
| Regression methods for modeling and prediction | Introduction Main techniques Artificial Neural Networks |
| Experimental methodology and result analysis | Methods for estimating error Results analysis Model comparison |
| Planning | ||||
| Methodologies / tests | Competencies / Results | Teaching hours (in-person & virtual) | 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 |
| 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. |
| Personalized attention |
|
|
| Assessment | |||
| Methodologies | Competencies / Results | 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 |
| 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. | 10 |
| Assessment comments | |||
|
First opportunity: To pass the course on the first opportunity, a minimum score of 50 must be achieved by adding up all the previous methodologies, being necessary to obtain a minimum of 30 in the Supervised Work and 20 in the sum of the Oral Presentation and the Analysis of documentary sources. Second opportunity: If the student does not pass the subject on the first opportunity, he/she must repeat the activities that are necessary from the method(s) that were not passed in the second call. For example, if a student passed the Oral Presentation + Analysis of documentary sources part, but failed the Supervised Work, he/she must repeat the practical work necessary to pass the course, normally those that were not individually passed. In the second opportunity, the minimum grade criteria established in the first call are maintained. Early opportunity For this opportunity, the same criteria are maintained as for the first, with the student having to specify delivery deadlines with the subject teachers. Students with part-time registration or academic exemption They may accumulate 15% of the grade corresponding to the Analysis of documentary sources in the oral presentation in the both opportunities. This modification must be requested from the professors of the subject at the beginning of the semester. Likewise, if they cannot make the oral presentation with the rest of the students, they must arrange an alternative date with the professors in all sessions. All regulatory aspects related to “academic exemption”, “dedication to study”, “permanence” and “academic fraud” will be governed in accordance with the current academic regulations of the UDC (https://www.udc.es/es/normativa/academica/) |
|||
| Sources of information |
| 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): |
|
| 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 |
| Recommendations | ||||
| Subjects that it is recommended to have taken before |
| Subjects that are recommended to be taken simultaneously | ||||
|
| Subjects that continue the syllabus |
| Other comments | |
|
1.- The delivery of the documentary works that are carried out in this subject: • 1.1. It will be requested in virtual format and/or computer support. • 1.2. It will be done through Moodle, in digital format without the need to print them • 1.3. If done on paper: - No plastic will be used. - Double-sided printing will be done. - Recycled paper will be used. - Printing drafts will be avoided. 2.- Sustainable use of resources must be made and negative impacts on the natural environment must be prevented. 3.- The importance of ethical principles related to the values ??of sustainability in personal and professional behavior must be taken into account. 4.- According to the different regulations applicable to university teaching, the gender perspective must be incorporated in this matter (non-sexist language will be used, bibliography by authors of both sexes will be used, the intervention of male and female students in class will be encouraged...). 5.- Work will be done to identify and modify sexist prejudices and attitudes, and the environment will be influenced to modify them and promote values ??of respect and equality. 6. Situations of discrimination based on gender must be detected and actions and measures will be proposed to correct them. 7. The full integration of students who, for physical, sensory, psychological or sociocultural reasons, experience difficulties in having suitable, equal and beneficial access to university life will be facilitated. |