Identifying Data 2022/23
Subject (*) Real Time Intelligent Systems  Code 614544026
Study programme
Máster Universitario en Intelixencia Artificial
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
First Optional 3
Language
English
Teaching method Face-to-face
Prerequisites
Department
Coordinador
Cabalar Fernandez, Jose Pedro
E-mail
pedro.cabalar@udc.es
Lecturers
Cabalar Fernandez, Jose Pedro
E-mail
pedro.cabalar@udc.es
Web
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
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
C2 CT02 - Command in understanding and expression, both in oral and written forms, of a foreign language
C4 CT04 - Acquiring a personal development for practicing a citizenship under observation of the democratic culture, the human rights and the gender perspective
C5 CT05 - Understanding the importance of the entrepreneurial culture and knowledge of the resources within the entrepreneur person's means
C6 CT06 - Acquiring abilities for life and healthy customs, routines and life styles

Learning aims
Learning outcomes Study programme competences
Knowing the features and functions of a real-time system AC7
AC8
AC9
AC13
AC14
AC15
AC19
AC20
AC21
AC22
BC1
BC2
BC5
BC6
BC9
BC10
CC2
CC4
CC5
CC6
Ability to design and program a real-time system AC7
AC8
AC9
AC13
AC14
AC15
AC19
AC22
AC27
BC7
BC9
BC10
CC2
CC4
CC5
CC6
Knowing the most common programming languages for real-time systems, both synchronous and asynchronous AC7
AC8
AC9
AC13
AC14
AC15
AC19
AC20
AC21
AC22
AC27
AC28
AC29
AC30
BC1
BC2
BC5
BC6
BC7
BC9
BC10
CC2
CC4
CC5
CC6
Knowing how to develop trustable software components, with special emphasis on fail tolerance and error recovery AC7
AC8
AC9
AC13
AC14
AC15
AC19
AC20
AC21
AC22
AC27
AC28
AC29
AC30
BC1
BC2
BC5
BC6
BC7
BC9
BC10
CC2
CC4
CC5
CC6

Contents
Topic Sub-topic
Real Time Systems Real Time Systems
Determinism and trustability Determinism and trustability
Paralelism Paralelism
Synchronous and asynchronous hipotheses Synchronous and asynchronous hipotheses
Implementation languages Implementation languages
Simulation Simulation
Behaviour verification Behaviour verification
Planning strategies Planning strategies
Architectures Architectures

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Laboratory practice A8 A9 A10 A14 A15 A16 A20 A21 A22 A23 A28 A29 A31 A30 B1 B2 B5 B6 B7 B9 B10 C2 C4 C5 C6 10.5 21 31.5
Objective test A8 A9 A10 A14 A15 A16 A20 A21 A22 A23 A28 A29 A31 A30 B1 B2 B5 B6 B7 B9 B10 C2 C4 C5 C6 1.5 10.5 12
Guest lecture / keynote speech A8 A9 A10 A14 A15 A16 A20 A21 A22 A23 A28 A29 A31 A30 B1 B2 B5 B6 B7 B9 B10 C2 C4 C5 C6 10.5 21 31.5
 
Personalized attention 0 0
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Laboratory practice Practical work, normally in groups, with tools of real time systems
Objective test Individual exam
Guest lecture / keynote speech Classes of concepts and foundations with small exercises

Personalized attention
Methodologies
Guest lecture / keynote speech
Laboratory practice
Objective test
Description
Tutorials and remote guidance by e-mail or online platform (Teams, moodle, etc)

Assessment
Methodologies Competencies Description Qualification
Guest lecture / keynote speech A8 A9 A10 A14 A15 A16 A20 A21 A22 A23 A28 A29 A31 A30 B1 B2 B5 B6 B7 B9 B10 C2 C4 C5 C6 Depending on how the course evolves, a part of the exam could be consolidated by submitting solved exercises along the lecture classes period 0.5
Laboratory practice A8 A9 A10 A14 A15 A16 A20 A21 A22 A23 A28 A29 A31 A30 B1 B2 B5 B6 B7 B9 B10 C2 C4 C5 C6 Submission of one or several practical assignments 49.5
Objective test A8 A9 A10 A14 A15 A16 A20 A21 A22 A23 A28 A29 A31 A30 B1 B2 B5 B6 B7 B9 B10 C2 C4 C5 C6 An individual exam consisting of several exercises that will be assessed up to a maximum of 50 points 50
 
Assessment comments

Sources of information
Basic

Complementary


Recommendations
Subjects that it is recommended to have taken before
Machine Learning I  /614544012
Deep Learning /614544013
Machine Learning II /614544014
Knowledge and Reasoning under Uncertainty/614544007
Reasoning and Planning /614544003

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus

Other comments


(*)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.