Study programme competencies |
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 |
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 |
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 |
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 |
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. |
Personalized attention |
Methodologies
|
Oral presentation |
ICT practicals |
Supervised projects |
|
Description |
During the ICT practical classes, the student will be allowed to ask the teacher any questions that arise about the programming of the learning methods.
Supervised projects: It is recommendable the use of a personal assistance in these activities to resolve conceptual doubts or procedures than can appear during the resolution of the practical problems. Also, the personal assistance will be focused on in the explanation, by the student, of the proposed solution.
Oral presentation: the students' progress in their theoretical work must be supervised by the teachers, both in terms of contents and format.
Students enrolled part-time will have an online personalised communication channel in all the methodologies. |
|
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 |
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.
|
Recommendations |
Subjects that it is recommended to have taken before |
|
Subjects that are recommended to be taken simultaneously |
Machine Vision for Industrial Applications/730497239 | Industrial Process Design and Optimization Project/730497236 | Machine Design and Construction/730497226 | Kinematics and Dynamics of Industrial Robots/730497228 |
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Subjects that continue the syllabus |
|
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. |
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