Identifying Data 2023/24
Subject (*) Intelligent IoT  Code 614544023
Study programme
Máster Universitario en Intelixencia Artificial
Descriptors Cycle Period Year Type Credits
Official Master's Degree 1st four-month period
 Second Optional 3
Language
English
Teaching method Face-to-face
Prerequisites
Department Enxeñaría de Computadores
Coordinador
E-mail
Lecturers
Blanco Novoa, Óscar
 E-mail
o.blanco@udc.es
Web http://http://campusvirtual.udc.gal/
General description

Study programme competencies
Code Study programme competences
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
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
 BC1
BC2
BC4
BC5
BC6
BC7
BC9
BC10
 CC5
CC8
CC9

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 Ordinary class hours 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 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.


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


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

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).


(*)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.