Mestrado Universitario en Enxeñaría Industrial (plan 2018)
Descriptors
Cycle
Period
Year
Type
Credits
Official Master's Degree
2nd four-month period
Second
Optional
4.5
Language
Spanish
Galician
English
Teaching method
Face-to-face
Prerequisites
Department
Ciencias da Computación e Tecnoloxías da Información Computación
Coordinador
Bellas Bouza, Francisco Javier
E-mail
francisco.bellas@udc.es
Lecturers
Bellas Bouza, Francisco Javier
E-mail
francisco.bellas@udc.es
Web
General description
Na materia de Robótica estúdanse os principais conceptos de robótica autónoma, facendo énfase no deseño automático de estratexias de control. Para iso, o contido da materia parte das estratexias clásicas de control para chegar ás máis actuais baseadas en conceptos da intelixencia computacional, tales como as redes neuronais, os algoritmos evolutivos e a aprendizaxe por reforzo.
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
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
Contents
Topic
Sub-topic
Introduction
Preliminary concepts.
Types of problems: classification, regression, clustering, anomaly detection, etc.
Forms of learning: supervised, unsupervised, reinforcement, etc.
Metrics for model evaluation
Methods for estimating error
Graphical methods for comparing results
Optimization and search
Heuristic and metaheuristic methods
Evolutionary algorithms
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
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
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.
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.
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.
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.
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
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
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
Subjects that continue the syllabus
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.
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.