Study programme competencies |
Code
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Study programme competences / results
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A8 |
CE07 - Ability to understand the consequences of the development of an explainable and interpretable intelligent system |
A9 |
CE08 - Ability to design and develop secure intelligent systems, in terms of integrity, confidentiality and robustness |
A10 |
CE09 - Ability to obtain a deep knowledge about fundamental principles and models of quantum computing and to apply them for the interpretation, selection, evaluation, modelling and creation of new concepts, theories, uses and technological developments related to Artificial Intelligence |
A14 |
CE13 - Knowledge of computer tools in the field of data analysis and statistical modelling and ability to select those ones most suitable for problem solving |
A15 |
CE14 - Understanding and command of the main machine learning techniques, including those devised for big volumes of data. Understanding and command of basic concepts and techniques for information search and filtering in big collections of data. |
A16 |
CE15 - Knowledge of computer tools in the field of machine learning and ability to select those ones most suitable for problem solving |
A20 |
CE19 - Knowledge of the different environments where AI based technologies can be applied and awareness of their capability to provide a differentiating added value |
A21 |
CE20 - Ability to combine and adapt different techniques, extrapolating knowledge among different application domains |
A22 |
CE21 - Knowledge of the techniques that facilitate the efficient organisation and management of AI projects in real environments, including resources management and tasks scheduling and taking into account the concepts of knowledge dissemination and open science |
A23 |
CE22 - Knowledge of the techniques that facilitate the security of data, applications and communications and the derived consequences on different application domains in AI |
A28 |
CE27 - Understanding the significance of the entrepreneurial culture and knowledge of the resources within the enterpreneur person's means |
A29 |
CE28 - Appropriate knowledge of the concept of enterprise, its organisation and management, and of the different business sectors, with the goal of providing solutions from the AI perspective |
A30 |
CE29 - Being able to apply knowledge, abilities and attitudes to the business and professional world, by planning, managing and evaluating projects in the scope of AI |
A31 |
CE30 - Being able to set out, model and solve problems that require the application of AI methods, techniques and technologies |
B1 |
CG01 - Maintaining and extending theoretical foundations to allow the introduction and exploitation of new and advanced technologies in the field of AI |
B2 |
CG02 - Successfully addressing each and every stage of an AI project |
B4 |
CG04 - Suitably elaborating written essays or motivated arguments, including some point of originality, writing plans, work projects, scientific papers and formulating reasonable hypotheses in the field |
B5 |
CG05 - Working in teams, especially of multidisciplinary nature, and being skilled in the management of time, people and decision making |
B6 |
CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context |
B7 |
CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study |
B9 |
CB04 - The students will be able to communicate their conclusions, their premises and their ultimate justifications, both to specialised and non-specialised audiences, using a clear style language, free from ambiguities |
B10 |
CB05 - The students will acquire learning abilities to allow them to continue studying in way that will mostly be self-directed or autonomous |
C5 |
CT05 - Understanding the importance of the entrepreneurial culture and knowledge of the resources within the entrepreneur person's means |
C8 |
CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society |
C9 |
CT09 - Being able to manage time and resources: outlining plans, prioritising activities, identifying criticisms, fixing deadlines and sticking to them |
Learning aims |
Learning outcomes |
Study programme competences / results |
To know and to analyze the implication of remote intelligent sensing in the environment. To know how decentralized data analysis techniques work in perimeter or federated learning environments. |
AC7 AC8 AC9 AC13 AC14 AC15 AC19 AC20 AC21 AC22 AC27 AC28 AC29 AC30
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BC1 BC2 BC4 BC5 BC6 BC7 BC9 BC10
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CC5 CC8 CC9
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Contents |
Topic |
Sub-topic |
Topic 1 |
Essential concepts of intelligent IoT |
Topic 2 |
Hardware and software platforms for intelligent IoT |
Topic 3 |
IoT protocols for the creation of intelligent systems |
Topic 4 |
Deploying AI in IoT devices: decentralized inference models |
Topic 5 |
Intelligent monitoring |
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Guest lecture / keynote speech |
A8 A10 A15 A28 B1 B6 C5 C8 |
10 |
10 |
20 |
ICT practicals |
A9 A14 A16 A21 A31 B2 B5 C9 |
11 |
11 |
22 |
Mixed objective/subjective test |
A20 A22 A23 A29 A30 B4 B7 B9 B10 |
1 |
31 |
32 |
|
Personalized attention |
|
1 |
0 |
1 |
|
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Guest lecture / keynote speech |
Lectures on the content of the subject |
ICT practicals |
ICT practicals to put in practice the concepts learned on the lectures |
Mixed objective/subjective test |
Test to assess the learned practical and theoretical concepts |
Personalized attention |
Methodologies
|
ICT practicals |
|
Description |
The professors will tutor the students and will guide them during the practical lessons and the supervised project. |
|
Assessment |
Methodologies
|
Competencies / Results |
Description
|
Qualification
|
ICT practicals |
A9 A14 A16 A21 A31 B2 B5 C9 |
Evaluation of the results and knowledge acquired during the ICT practicals |
50 |
Mixed objective/subjective test |
A20 A22 A23 A29 A30 B4 B7 B9 B10 |
Evaluation of the competences acquired in the subject |
50 |
|
Assessment comments |
FIRST CALL The evaluation of the subject will be carried out through a
final written test, which will account for 50% of the final grade. The
completion of the ICT practicals practices is mandatory to pass the
course and will suppose 50% of the final grade. This evaluation will be
carried out based on the work developed in the laboratory. To pass the
course, a minimum grade of 4 points out of 10 will be required for both
the written exam and the ICT practicals. The student swho do not take
the final written test will be reflected in the university grade record
as NOT PRESENTED. Part-time students and with attendance exemption
academic waiver: it will not be required the attendance to the practical
lessons, which will be flexible with the delivery and defence dates. In
the same way, tutoring will be adapted to the scheduling restrictions
of the part-time students. SECOND CALL In case of failing the subject
in the first call, the evaluation obtained on the practical activity
carried out will be maintained for the second opportunity if the grade
is higher than 50% of the awarded grade. In the event that it is not
greater than 50%, the student, on the second opportunity, will take a
written test, which will account for 70% of the grade, and a practical
exam, which will provide the other 30%. The fraudulent performance of
tests or assessment activities, once verified, will directly involve the
qualification of failed in the call in which it is committed: the
student will be qualified with "failed" (numerical grade 0) in the
corresponding call of the academic year, both if the offense is
committed in the first opportunity as in the second. For this, the
qualification will be modified in the first opportunity report, if
necessary. The previously described evaluation process applies to both
new enrollment students and repeating students.
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Sources of information |
Basic
|
S. P. Yadav, B. S. Bhati, D. P. Mahato, S. Kumar (2022). Federated Learning for IoT Applications. Springer
Jan Holler, Vlasios Tsiatsis, Catherine Mulligan, Stefan Avesand, Stamatis Karnouskos David Boyle (2014). From Machine-to-Machine to the Internet of Things: Introduction to a new Age of Intelligence. Academic Press
Peter Waher (2015). Learning Internet of Things. Packt Publishing
Samuel Greengard (2015). The Internet of Things. MIT Press |
|
Complementary
|
Adrian McEwen, Hakim Cassimally (2013). Designing the Internet of Things. Wiley
Vijay Madisetti, Arshdeep Bahga (2014). Internet of Things (A Hands-on-Approach). VPT |
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Recommendations |
Subjects that it is recommended to have taken before |
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Subjects that are recommended to be taken simultaneously |
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Subjects that continue the syllabus |
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Other comments |
Due to the high correlation between the concepts developed in the lectures and the contents of the practicals, it is recommended the students perseverance in the study of the subject, attending the practical sessions with the concepts already worked on. With the completion of the ICT practicals such concepts will be consolidated, thus facilitating the study and understanding of the subject. This subject will comply with the different regulations for university teaching, respecting the gender perspective (e.g. non-sexist language will be used). |
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