The BSc in Artificial Intelligence provides the broad, deep and multidisciplinary training that professionals in this field need and that is essential to successfully build the intelligent services and applications that are having such an important impact on our lives at all levels.
Duration:
4 academic years
RUCT code: 2504532
Seats number: 50
Dean or center director:
JULIA GONZALEZ ALVAREZ
julia.gonzalez [at] usc.es
Title coordinator:
Alberto Jose Bugarin Diz
alberto.bugarin.diz [at] usc.es
Use languages:
Galician, Spanish
MECES Level: 2
Coordinator university:
University of Santiago de Compostela
Partaker universities:
University of Santiago de Compostela
University of A Coruña
University of Vigo
Xunta de Galicia title implantation authorization date:
Orde do 27/07/2022 (DOG do 10/08/2022)
BOE publication date:
BOE do 26/04/2023
Last accreditation date:
27/06/2022
This is an interuniversity degree of four courses (240 ECTS), coordinated by the USC, in which the subjects of the first two courses are common to the three universities. In the third and fourth years, at USC we develop the specialization in Intelligent Technologies, with an interdisciplinary vision that will allow the development of successful applications and services that integrate models and technologies from Artificial Intelligence with others from natural intelligences and behaviors. The specialization in Intelligent Technologies also includes a professional module that allows an important part of the course to be taken in a business environment and to develop skills that are highly valued in this field.
The degree is structured in such a way that the contents of the first two years are common to the three universities and in the last two years each university develops its own itinerary that includes a series of linked electives:
USC Itinerary: Intelligent Technologies
This pathway is made up of 120 credits in the 3rd and 4th years, of which 114 credits are optional credits linked to the pathway (OPV) and students will have to take 6 additional optional credits (OP) for which they can choose to extend the External Work Placement or choose the open elective.
UDC Itinerary: Intelligent Society and Enterprise
In this pathway, students must take 120 credits distributed between the 3rd and 4th years, at a rate of 106.5 optional credits linked to the pathway itself (OPV) and 13.5 optional credits (OP). In the fourth year of the pathway, two modalities are offered: a transversal module of academic training and a transversal module of dual training. Dual training involves the acquisition of a series of skills through direct training in companies, in coordination with the University and with personalised monitoring by two tutors: the academic and the business tutor. Students enrolled in dual training will take 48 of 60 ECTS credits in the company, including all the optional credits in an External Internship II subject. Students enrolled in academic training will have to choose 3 optional subjects from a choice of 9 subjects.
Itinerary UVIGO: Intelligent Information Systems
This itinerary is made up of 120 credits differentiated in 3rd and 4th year, in which 108 credits are optional credits linked to the itinerary (OPV) and students will have to choose 12 optional credits (OP) (2 subjects) from an annual offer of 4 subjects.
1.- El alumnado de primer curso por primera vez a tiempo completo tiene que matricular 60 créditos.
Un 15% del alumnado podrá cursar estudios a tiempo parcial (30 créditos).
2.- Continuación de estudios : libre con un máximo de 75 créditos.
In addition to the welcome and presentation day, continuous attention is offered in each centre. The addresses or dean's offices of the centres and their administrative services are accessible on a daily basis for any academic queries that affect their studies. The degree coordinators are the natural link with students for support and guidance related to their Bachelor's or Master's degree studies. Each centre has information screens where information of interest is distributed (announcements, grants, employment, seminars, conferences, etc.). Other means of information are the notice boards, where class timetables, exams and other announcements (regulations, mobility programmes, work placements, etc.) are posted.
The website of each centre is kept permanently updated as a basic reference for information, where academic activity timetables, assessment calendars, subject programmes, teaching staff tutoring hours, extraordinary activities, regulations, etc. can be consulted. Also, within the virtual campus of each university, specific virtual classrooms are set up for the coordination of the degrees, which are a meeting point for teaching staff and students.
The USC has a student-tutor program for the undergraduate degrees, so that final year students, after receiving training from the University, can perform orientational tasks to students initiating their studies.
Information about student-tutor program:
When a degree suspension occurs, the USC guarantees the adequate development of teachings that were initiated by their students until its suspension. For that, the Government Council approves the criteria related with the admission of new degree entry registration and the gradual suspension of teaching impartation, among others.
If the suspended degree is substituted for a similar one —modifying the nature of the degree—, the new degree regulations will set the conditions to facilitate students the continuity of the new degree’s studies. These regulations will also set subject equivalences in both programs.
Generals for undergraduate degrees
Special access conditions or tests are not contemplated
The School of Engineering (ETSE) currently has teaching classrooms in two buildings located on the USC campus (ETSE building and Monte de la Condesa building), and new spaces are planned for the Emprendia Building, in the Ciudad de la Salud area, close to the main ETSE building. In addition to theory and computer classrooms, and services such as the Library and the Assembly Hall, there will be workrooms and work areas with free access, and versatile spaces will be set up to organize interactive sessions with laptops and tutoring seminars.
The Degree in Artificial intelligence faces the challenge of training professionals, with abilities, knowledge and skills that allow them to create new intelligent applications or services or to provide valuable innovations with the adequate, professional and responsible use of artificial inteligence.
- Students must demonstrate possession and understanding of knowledge in an area of study draw from the premise of a general secondary education. It is usually found in a level that —although it can be supported by advanced text books— also includes some aspects that imply knowledge arising from the forefront of their area of study.
- Students must be able to apply their knowledge to their work or vocation in a professional way and possess the competences which are usually demonstrated by means of the elaboration and defence of arguments and problem solving within their area of study
- Students must have the ability to collect and interpret relevant data —normally within their area of study— in order to make judgements that include a reflection on relevant themes of social, scientific or ethic nature.
- Students must be able to transmit information, ideas, problems and solutions to a public, both specialized and non-specialized.
- Students must develop those abilities of learning necessary to start higher studies with a high degree of autonomy
- Ability to conceive, write, organize, plan, and develop models, applications and services in the field of artificial intelligence, identifying objectives, priorities, deadlines, resources and risks, and controlling the established processes.
- Ability to solve problems with initiative, decision-making, autonomy and creativity.
- Ability to design and create quality models and solutions based on Artificial intelligence that are efficient, solid, transparent and responsible.
- Ability to select and justify the appropriate methods and techniques to solve a specific problem, or to develop and provide new methods based on artificial intelligence.
- Ability to conceive new computational systems and/or evaluate the performance of existing systems that integrate models and techniques of artificial intelligence.
- Ability to use mathematical concepts and methods that that may arise in the modeling and solving of artificial intelligence problems.
- Ability to use probability, statistics and optimization concepts and methods to model and solve the problems of artificial intelligence.
- Ability to solve artificial intelligence problems that need algorithms, from its design and implementation to its evaluation.
- To know and apply software engineering and user-centered design methodologies to the field of artificial intelligence.
- Ability to understand and master the basic concepts of logic, grammars and formal languages to analyze and improve solutions based on artificial intelligence.
- To know the structure, organization, functioning and interconnection of informatic systems (computer, operative systems and computer networks).
- To understand and apply the basic principles and techniques of parallel and distributed programming for the development and efficient execution of artificial intelligence techniques.
- Ability to do analysis, design, implementation of applications that require working with big volumes of data, and applying adequate hardware/software architectures.
- Ability to deploy in the cloud artificial intelligence applications that run efficiently with defined computational resources.
- To understand the data capture, storage and processing needs in the context of the Internet of Things, understanding the heterogeneity of the data and the special characteristics of this type of environment.
- To know the main platforms and software architectures for the acquisition, storage and processing of data in the context of the Internet of Things.
- To know and apply the characteristics, functionalities and structure of database systems and the distributed databases, that allow its adequate use and the implementation of Artificial Intelligence solutions that can include large volumes of data.
- Ability to define and interpret the fundamentals of organizations, the basic aspects of its organization and management, the process of innovation and its management, its different functional areas and its socioeconomic environment.
- To understand new business and innovation models in the framework of companies based on artificial intelligence and its technologies.
- Ability To design and create models of economic-financial valuation of projects using appropriate computer tools.
- Ability to adapt and apply a significant set of the competencies acquired in this degree in the professional field .
- To know the fundamentals of algorithms of artificial intelligence and optimization, to understand its computational complexity and to know how to apply them to solve problems.
- To know the basic aspects of metaheuristic and bio-inspired algorithms for problem solving, have the ability to apply them and to design new models.
- To know the modelization and representation of knowledge techniques and its relationship with the reasoning paradigms, designing solutions based on logical reasoning that take into account efficiency and the needs of the problems.
- Ability to design systems based on knowledge of strategies of representation and reasoning applied to different domains and problems, discovering the basic problems that arise in their construction.
- To know semantic technologies for storing and accessing knowledge graphs and their use in problem solving.
- To know the fundamentals of approximate reasoning and decision-making techniques in uncertainty environments, selecting the most adequate one for problem solving.
- To conceive, design, develop and present solutions to problems of a certain complexity based on artificial intelligence, facing and solving the difficulties that may arise during its development in an adequate way.
- To know and apply correctly the validation techniques for artificial intelligence solutions.
- development of adequate abilities to carry out an original exercise, present it and defend it before a university tribunal, consisting of a project in the field of Artificial Intelligence technologies in which the competences acquired in the courses are synthesized and integrated.
- Ability to communicate and transmit their knowledge, abilities and skills.
- Ability to work in teams, in interdisciplinary environments and managing conflicts.
- Ability to create new models and solutions in an autonomous and creative way, adapting to new situations. Initiative and entrepreneurial spirit.
- Ability to introduce gender perspective in models, techniques and solutions based on artificial intelligence.
- Ability to develop models, techniques and solutions based on artificial intelligence that are ethical, non-discriminatory and reliable.
- Ability to integrate legal, social, environmental and economic aspects that are intrinsic to artificial intelligence, analyzing their impact and committing to the search of compatible solutions for sustainable development.
Student mobility is carried out from the second year of studies in the degree, in four-monthly or annual periods. The selection of candidates is carried out, for each call or programme, according to the regulations of each university. At the USC, it is made up of the person from the management team responsible for exchange programmes, the person responsible for the UAGCD and the people who act as academic coordinators, in accordance with previously established selection criteria, which take into account the academic record, a report and, where appropriate, the language skills required by the host university.
Student mobility is regulated through the “Regulation of inter-university exchange.” Exchange programs are managed through the International Relations Office, such as national exchange programs (SICUE) as well as Europeans (ERASMUS) and from outside the European Union (exchanges with Latin American countries or English-speaking countries):
The Degree Syllabus in Artificial Intelligence includes the recognition of 6 compulsory credits for external internships, which will involve a total of 150 hours of face-to-face work in the organisation offering the internships.
The ETSE has experience in the organisation and direct management of these internships, coordinated through the respective Degree Committees. The external work placement programme has a coordinator for each degree who is responsible for promoting the offer, supervising the selection and guaranteeing its correct operation. The coordinators are assisted by a team of tutors who act as the most direct interlocutors with the external entities and help students as necessary during the work placement.
To carry out the work placement, the student must have an external tutor in the company and an academic tutor responsible for establishing, in coordination with the external tutor, the work placement programme for each student according to the characteristics of the work to be carried out, monitoring and guiding the student during the work placement and assessing the student, according to the work placement report to be submitted and the report issued by the external tutor.
The objective of the Final Degree Project will be students' completion of an original project in which the acquisition of the skills and competences described above in the general objectives of the degree can be verified, together with specific academic, research or professional orientation skills.
Depending on the type of work, the activities to be carried out may consist of a series of stages, including: Bibliographic study, Definition of objectives, Planning, Analysis of scientific-technological alternatives, Design and Implementation of Solutions, Validation and Testing, Documentation, Communication of Results.
|
Indicator |
2022-2023 |
2023-2024 |
|
|---|---|---|---|
|
Offer |
|||
|
IN01 Places offered. Number of Places Offered for Each Academic Year Scale info: Whole Number |
50,0 |
50,0 |
|
|
Enrolment |
|||
|
IN02 Enrolment. Number of Students Enrolled in an Academic Year Excluding Incoming Mobility Program Students Scale info: Whole Number |
54,0 |
102,0 |
|
|
IN03 New enrolments. Number of Students Enrolling in a Study Program for the First Time, Including Those Transferring Their Academic Records, Accessing Through Partial Recognition of Foreign Studies, or Transitioning from Phasing-Out Programs Scale info: Whole Number |
54,0 |
55,0 |
|
|
IN04 Enrolment for start of studies. Number of Students Enrolling in the First Year of a Study Program for the First Time, Excluding Those Accessing Through Partial Recognition of Foreign Studies, Transfers, or Transitions from Phasing-Out Programs Scale info: Whole Number |
54,0 |
52,0 |
|
|
Admittance profile |
|||
|
IN06 Average pre-enrolment access score. Average Admission Grade through Pre-registration for Students Beginning Studies, Determining the Entry Profile. Scale info: Rational number with two decimals. The range goes from 0 to the maximum value that can be obtained in each academic year for pre-registration (some historical values were 10.00 points, 12.00 points, or 14.00 points). |
11,8682 |
12,1825 |
|
|
IN08 Percentage of foreign students over enrolled students. Percentage of foreign students among enrolled students, excluding those enrolled in incoming mobility programs. Scale info: Percentage with Two Decimal Places |
0,0 |
0,0 |
|
|
IN09 Percentage of national students from outside Galicia over enrolled students. Percentage of national students from outside Galicia among enrolled students, excluding those enrolled in incoming mobility programs. Scale info: Percentage with Two Decimal Places |
0,0 |
3,92 |
|
|
Adaptation to demand |
|||
|
IN10 Ratio of students pre-enrolled in 1st option over the places offered. Number of first-time enrolled students who pre-registered as their first choice divided by the number of available seats. Scale info: Percentage with Two Decimal Places |
72,0 |
84,0 |
|
|
IN12 Occupancy rate Number of new enrollment students through pre-registration divided by the number of available seats. Scale info: Percentage with Two Decimal Places |
108,0 |
104,0 |
|
Indicator |
2022-2023 |
2023-2024 |
|
|---|---|---|---|
|
External mobility |
|||
|
IN18 Percentage of students received by the USC from mobility programmes over the total number of enrolled students. Number of students received at USC in that degree from other universities (mobility programs) divided by the number of students enrolled in the degree. Scale info: Percentage with two decimal places. |
0,0 |
0,0 |
|
Indicator |
2022-2023 |
2023-2024 |
|
|---|---|---|---|
|
Assessment |
|||
|
IN36 Assessment rate. Percentage ratio between the total number of ordinary credits students presented themselves for and the total number of ordinary credits they enrolled in. Scale info: Percentage with two decimal places. |
92,72 |
95,06 |
|
|
Students per group |
|||
|
IN32 Average number of students per interactive teaching group. Ratio between the number of enrolled students and the number of interactive teaching groups. Scale info: Rational number with two decimal places. |
26,8 |
20,68 |
|
|
IN55 Average number of students enrolled in subjects (FB and OB) per theory (lecture) group. Ratio between the number of students enrolled in compulsory basic training subjects and the number of theory groups for those subjects. Scale info: Rational number with two decimal places. |
53,6 |
51,7 |
|
|
Success |
|||
|
IN35 Success rate. Percentage ratio between the total number of ordinary credits passed by students and the total number of ordinary credits they presented themselves for. Scale info: Percentage with two decimal places. |
86,32 |
92,87 |
|
|
Performance |
|||
|
IN34 Performance rate. Percentage ratio between the total number of ordinary credits passed by students and the total number of ordinary credits in which they enrolled. Scale info: Percentage with two decimal places. |
80,04 |
88,29 |
|
|
Satisfaction |
|||
|
IN46 Student satisfaction with the teaching received. Average rating of the student satisfaction survey with the teaching received. Scale info: Minimum 0.00, maximum 5.00. |
3,86 |
3,97 |
|
|
IN47 Satisfaction of the teaching staff with the teaching given. Average rating of the faculty satisfaction survey with the teaching provided. Scale info: Minimum 0.00, maximum 5.00. |
4,1481 |
4,1284 |
|
|
IN48 Response rate in the survey on student satisfaction with the teaching received. Percentage of responses in the student satisfaction survey. Scale info: Percentage with two decimal places. |
41,8182 |
79,8077 |
|
Indicator |
2022-2023 |
|---|---|
|
IN24 Percentage of teaching and research staff with six-year terms over the total teaching and research staff in the degree. Percentage ratio between the PDI (Teaching and Research Staff) with research periods (sexenios) and the total PDI with teaching in the degree who may have research periods. Scale info: Percentage with two decimal places. |
83,33 |
|
IN25 Percentage of PhD teaching and research staff over total teaching and research staff. Percentage ratio between the doctoral PDI (Teaching and Research Staff) and the total PDI with teaching in the degree. Scale info: Percentage with two decimal places. |
100,0 |
|
IN26 Percentage of civil servant teaching and research staff over total teaching and research staff. Percentage ratio between the tenured PDI (Teaching and Research Staff) and the total PDI with teaching in the degree. Scale info: Percentage with two decimal places. |
62,5 |
Duration:
4 academic years
RUCT code: 2504532
Seats number: 50
Dean or center director:
JULIA GONZALEZ ALVAREZ
julia.gonzalez [at] usc.es
Title coordinator:
Alberto Jose Bugarin Diz
alberto.bugarin.diz [at] usc.es
Use languages:
Galician, Spanish
MECES Level: 2
Coordinator university:
University of Santiago de Compostela
Partaker universities:
University of Santiago de Compostela
University of A Coruña
University of Vigo
Xunta de Galicia title implantation authorization date:
Orde do 27/07/2022 (DOG do 10/08/2022)
BOE publication date:
BOE do 26/04/2023
Last accreditation date:
27/06/2022
This is an interuniversity degree of four courses (240 ECTS), coordinated by the USC, in which the subjects of the first two courses are common to the three universities. In the third and fourth years, at USC we develop the specialization in Intelligent Technologies, with an interdisciplinary vision that will allow the development of successful applications and services that integrate models and technologies from Artificial Intelligence with others from natural intelligences and behaviors. The specialization in Intelligent Technologies also includes a professional module that allows an important part of the course to be taken in a business environment and to develop skills that are highly valued in this field.
The degree is structured in such a way that the contents of the first two years are common to the three universities and in the last two years each university develops its own itinerary that includes a series of linked electives:
USC Itinerary: Intelligent Technologies
This pathway is made up of 120 credits in the 3rd and 4th years, of which 114 credits are optional credits linked to the pathway (OPV) and students will have to take 6 additional optional credits (OP) for which they can choose to extend the External Work Placement or choose the open elective.
UDC Itinerary: Intelligent Society and Enterprise
In this pathway, students must take 120 credits distributed between the 3rd and 4th years, at a rate of 106.5 optional credits linked to the pathway itself (OPV) and 13.5 optional credits (OP). In the fourth year of the pathway, two modalities are offered: a transversal module of academic training and a transversal module of dual training. Dual training involves the acquisition of a series of skills through direct training in companies, in coordination with the University and with personalised monitoring by two tutors: the academic and the business tutor. Students enrolled in dual training will take 48 of 60 ECTS credits in the company, including all the optional credits in an External Internship II subject. Students enrolled in academic training will have to choose 3 optional subjects from a choice of 9 subjects.
Itinerary UVIGO: Intelligent Information Systems
This itinerary is made up of 120 credits differentiated in 3rd and 4th year, in which 108 credits are optional credits linked to the itinerary (OPV) and students will have to choose 12 optional credits (OP) (2 subjects) from an annual offer of 4 subjects.
1.- El alumnado de primer curso por primera vez a tiempo completo tiene que matricular 60 créditos.
Un 15% del alumnado podrá cursar estudios a tiempo parcial (30 créditos).
2.- Continuación de estudios : libre con un máximo de 75 créditos.
In addition to the welcome and presentation day, continuous attention is offered in each centre. The addresses or dean's offices of the centres and their administrative services are accessible on a daily basis for any academic queries that affect their studies. The degree coordinators are the natural link with students for support and guidance related to their Bachelor's or Master's degree studies. Each centre has information screens where information of interest is distributed (announcements, grants, employment, seminars, conferences, etc.). Other means of information are the notice boards, where class timetables, exams and other announcements (regulations, mobility programmes, work placements, etc.) are posted.
The website of each centre is kept permanently updated as a basic reference for information, where academic activity timetables, assessment calendars, subject programmes, teaching staff tutoring hours, extraordinary activities, regulations, etc. can be consulted. Also, within the virtual campus of each university, specific virtual classrooms are set up for the coordination of the degrees, which are a meeting point for teaching staff and students.
The USC has a student-tutor program for the undergraduate degrees, so that final year students, after receiving training from the University, can perform orientational tasks to students initiating their studies.
Information about student-tutor program:
When a degree suspension occurs, the USC guarantees the adequate development of teachings that were initiated by their students until its suspension. For that, the Government Council approves the criteria related with the admission of new degree entry registration and the gradual suspension of teaching impartation, among others.
If the suspended degree is substituted for a similar one —modifying the nature of the degree—, the new degree regulations will set the conditions to facilitate students the continuity of the new degree’s studies. These regulations will also set subject equivalences in both programs.
Generals for undergraduate degrees
Special access conditions or tests are not contemplated
The School of Engineering (ETSE) currently has teaching classrooms in two buildings located on the USC campus (ETSE building and Monte de la Condesa building), and new spaces are planned for the Emprendia Building, in the Ciudad de la Salud area, close to the main ETSE building. In addition to theory and computer classrooms, and services such as the Library and the Assembly Hall, there will be workrooms and work areas with free access, and versatile spaces will be set up to organize interactive sessions with laptops and tutoring seminars.
The Degree in Artificial intelligence faces the challenge of training professionals, with abilities, knowledge and skills that allow them to create new intelligent applications or services or to provide valuable innovations with the adequate, professional and responsible use of artificial inteligence.
- Students must demonstrate possession and understanding of knowledge in an area of study draw from the premise of a general secondary education. It is usually found in a level that —although it can be supported by advanced text books— also includes some aspects that imply knowledge arising from the forefront of their area of study.
- Students must be able to apply their knowledge to their work or vocation in a professional way and possess the competences which are usually demonstrated by means of the elaboration and defence of arguments and problem solving within their area of study
- Students must have the ability to collect and interpret relevant data —normally within their area of study— in order to make judgements that include a reflection on relevant themes of social, scientific or ethic nature.
- Students must be able to transmit information, ideas, problems and solutions to a public, both specialized and non-specialized.
- Students must develop those abilities of learning necessary to start higher studies with a high degree of autonomy
- Ability to conceive, write, organize, plan, and develop models, applications and services in the field of artificial intelligence, identifying objectives, priorities, deadlines, resources and risks, and controlling the established processes.
- Ability to solve problems with initiative, decision-making, autonomy and creativity.
- Ability to design and create quality models and solutions based on Artificial intelligence that are efficient, solid, transparent and responsible.
- Ability to select and justify the appropriate methods and techniques to solve a specific problem, or to develop and provide new methods based on artificial intelligence.
- Ability to conceive new computational systems and/or evaluate the performance of existing systems that integrate models and techniques of artificial intelligence.
- Ability to use mathematical concepts and methods that that may arise in the modeling and solving of artificial intelligence problems.
- Ability to use probability, statistics and optimization concepts and methods to model and solve the problems of artificial intelligence.
- Ability to solve artificial intelligence problems that need algorithms, from its design and implementation to its evaluation.
- To know and apply software engineering and user-centered design methodologies to the field of artificial intelligence.
- Ability to understand and master the basic concepts of logic, grammars and formal languages to analyze and improve solutions based on artificial intelligence.
- To know the structure, organization, functioning and interconnection of informatic systems (computer, operative systems and computer networks).
- To understand and apply the basic principles and techniques of parallel and distributed programming for the development and efficient execution of artificial intelligence techniques.
- Ability to do analysis, design, implementation of applications that require working with big volumes of data, and applying adequate hardware/software architectures.
- Ability to deploy in the cloud artificial intelligence applications that run efficiently with defined computational resources.
- To understand the data capture, storage and processing needs in the context of the Internet of Things, understanding the heterogeneity of the data and the special characteristics of this type of environment.
- To know the main platforms and software architectures for the acquisition, storage and processing of data in the context of the Internet of Things.
- To know and apply the characteristics, functionalities and structure of database systems and the distributed databases, that allow its adequate use and the implementation of Artificial Intelligence solutions that can include large volumes of data.
- Ability to define and interpret the fundamentals of organizations, the basic aspects of its organization and management, the process of innovation and its management, its different functional areas and its socioeconomic environment.
- To understand new business and innovation models in the framework of companies based on artificial intelligence and its technologies.
- Ability To design and create models of economic-financial valuation of projects using appropriate computer tools.
- Ability to adapt and apply a significant set of the competencies acquired in this degree in the professional field .
- To know the fundamentals of algorithms of artificial intelligence and optimization, to understand its computational complexity and to know how to apply them to solve problems.
- To know the basic aspects of metaheuristic and bio-inspired algorithms for problem solving, have the ability to apply them and to design new models.
- To know the modelization and representation of knowledge techniques and its relationship with the reasoning paradigms, designing solutions based on logical reasoning that take into account efficiency and the needs of the problems.
- Ability to design systems based on knowledge of strategies of representation and reasoning applied to different domains and problems, discovering the basic problems that arise in their construction.
- To know semantic technologies for storing and accessing knowledge graphs and their use in problem solving.
- To know the fundamentals of approximate reasoning and decision-making techniques in uncertainty environments, selecting the most adequate one for problem solving.
- To conceive, design, develop and present solutions to problems of a certain complexity based on artificial intelligence, facing and solving the difficulties that may arise during its development in an adequate way.
- To know and apply correctly the validation techniques for artificial intelligence solutions.
- development of adequate abilities to carry out an original exercise, present it and defend it before a university tribunal, consisting of a project in the field of Artificial Intelligence technologies in which the competences acquired in the courses are synthesized and integrated.
- Ability to communicate and transmit their knowledge, abilities and skills.
- Ability to work in teams, in interdisciplinary environments and managing conflicts.
- Ability to create new models and solutions in an autonomous and creative way, adapting to new situations. Initiative and entrepreneurial spirit.
- Ability to introduce gender perspective in models, techniques and solutions based on artificial intelligence.
- Ability to develop models, techniques and solutions based on artificial intelligence that are ethical, non-discriminatory and reliable.
- Ability to integrate legal, social, environmental and economic aspects that are intrinsic to artificial intelligence, analyzing their impact and committing to the search of compatible solutions for sustainable development.
Student mobility is carried out from the second year of studies in the degree, in four-monthly or annual periods. The selection of candidates is carried out, for each call or programme, according to the regulations of each university. At the USC, it is made up of the person from the management team responsible for exchange programmes, the person responsible for the UAGCD and the people who act as academic coordinators, in accordance with previously established selection criteria, which take into account the academic record, a report and, where appropriate, the language skills required by the host university.
Student mobility is regulated through the “Regulation of inter-university exchange.” Exchange programs are managed through the International Relations Office, such as national exchange programs (SICUE) as well as Europeans (ERASMUS) and from outside the European Union (exchanges with Latin American countries or English-speaking countries):
The Degree Syllabus in Artificial Intelligence includes the recognition of 6 compulsory credits for external internships, which will involve a total of 150 hours of face-to-face work in the organisation offering the internships.
The ETSE has experience in the organisation and direct management of these internships, coordinated through the respective Degree Committees. The external work placement programme has a coordinator for each degree who is responsible for promoting the offer, supervising the selection and guaranteeing its correct operation. The coordinators are assisted by a team of tutors who act as the most direct interlocutors with the external entities and help students as necessary during the work placement.
To carry out the work placement, the student must have an external tutor in the company and an academic tutor responsible for establishing, in coordination with the external tutor, the work placement programme for each student according to the characteristics of the work to be carried out, monitoring and guiding the student during the work placement and assessing the student, according to the work placement report to be submitted and the report issued by the external tutor.
The objective of the Final Degree Project will be students' completion of an original project in which the acquisition of the skills and competences described above in the general objectives of the degree can be verified, together with specific academic, research or professional orientation skills.
Depending on the type of work, the activities to be carried out may consist of a series of stages, including: Bibliographic study, Definition of objectives, Planning, Analysis of scientific-technological alternatives, Design and Implementation of Solutions, Validation and Testing, Documentation, Communication of Results.
|
Indicator |
2022-2023 |
2023-2024 |
|
|---|---|---|---|
|
Offer |
|||
|
IN01 Places offered. Number of Places Offered for Each Academic Year Scale info: Whole Number |
50,0 |
50,0 |
|
|
Enrolment |
|||
|
IN02 Enrolment. Number of Students Enrolled in an Academic Year Excluding Incoming Mobility Program Students Scale info: Whole Number |
54,0 |
102,0 |
|
|
IN03 New enrolments. Number of Students Enrolling in a Study Program for the First Time, Including Those Transferring Their Academic Records, Accessing Through Partial Recognition of Foreign Studies, or Transitioning from Phasing-Out Programs Scale info: Whole Number |
54,0 |
55,0 |
|
|
IN04 Enrolment for start of studies. Number of Students Enrolling in the First Year of a Study Program for the First Time, Excluding Those Accessing Through Partial Recognition of Foreign Studies, Transfers, or Transitions from Phasing-Out Programs Scale info: Whole Number |
54,0 |
52,0 |
|
|
Admittance profile |
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|
IN06 Average pre-enrolment access score. Average Admission Grade through Pre-registration for Students Beginning Studies, Determining the Entry Profile. Scale info: Rational number with two decimals. The range goes from 0 to the maximum value that can be obtained in each academic year for pre-registration (some historical values were 10.00 points, 12.00 points, or 14.00 points). |
11,8682 |
12,1825 |
|
|
IN08 Percentage of foreign students over enrolled students. Percentage of foreign students among enrolled students, excluding those enrolled in incoming mobility programs. Scale info: Percentage with Two Decimal Places |
0,0 |
0,0 |
|
|
IN09 Percentage of national students from outside Galicia over enrolled students. Percentage of national students from outside Galicia among enrolled students, excluding those enrolled in incoming mobility programs. Scale info: Percentage with Two Decimal Places |
0,0 |
3,92 |
|
|
Adaptation to demand |
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|
IN10 Ratio of students pre-enrolled in 1st option over the places offered. Number of first-time enrolled students who pre-registered as their first choice divided by the number of available seats. Scale info: Percentage with Two Decimal Places |
72,0 |
84,0 |
|
|
IN12 Occupancy rate Number of new enrollment students through pre-registration divided by the number of available seats. Scale info: Percentage with Two Decimal Places |
108,0 |
104,0 |
|
Indicator |
2022-2023 |
2023-2024 |
|
|---|---|---|---|
|
External mobility |
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|
IN18 Percentage of students received by the USC from mobility programmes over the total number of enrolled students. Number of students received at USC in that degree from other universities (mobility programs) divided by the number of students enrolled in the degree. Scale info: Percentage with two decimal places. |
0,0 |
0,0 |
|
Indicator |
2022-2023 |
2023-2024 |
|
|---|---|---|---|
|
Assessment |
|||
|
IN36 Assessment rate. Percentage ratio between the total number of ordinary credits students presented themselves for and the total number of ordinary credits they enrolled in. Scale info: Percentage with two decimal places. |
92,72 |
95,06 |
|
|
Students per group |
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|
IN32 Average number of students per interactive teaching group. Ratio between the number of enrolled students and the number of interactive teaching groups. Scale info: Rational number with two decimal places. |
26,8 |
20,68 |
|
|
IN55 Average number of students enrolled in subjects (FB and OB) per theory (lecture) group. Ratio between the number of students enrolled in compulsory basic training subjects and the number of theory groups for those subjects. Scale info: Rational number with two decimal places. |
53,6 |
51,7 |
|
|
Success |
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|
IN35 Success rate. Percentage ratio between the total number of ordinary credits passed by students and the total number of ordinary credits they presented themselves for. Scale info: Percentage with two decimal places. |
86,32 |
92,87 |
|
|
Performance |
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|
IN34 Performance rate. Percentage ratio between the total number of ordinary credits passed by students and the total number of ordinary credits in which they enrolled. Scale info: Percentage with two decimal places. |
80,04 |
88,29 |
|
|
Satisfaction |
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|
IN46 Student satisfaction with the teaching received. Average rating of the student satisfaction survey with the teaching received. Scale info: Minimum 0.00, maximum 5.00. |
3,86 |
3,97 |
|
|
IN47 Satisfaction of the teaching staff with the teaching given. Average rating of the faculty satisfaction survey with the teaching provided. Scale info: Minimum 0.00, maximum 5.00. |
4,1481 |
4,1284 |
|
|
IN48 Response rate in the survey on student satisfaction with the teaching received. Percentage of responses in the student satisfaction survey. Scale info: Percentage with two decimal places. |
41,8182 |
79,8077 |
|
Indicator |
2022-2023 |
|---|---|
|
IN24 Percentage of teaching and research staff with six-year terms over the total teaching and research staff in the degree. Percentage ratio between the PDI (Teaching and Research Staff) with research periods (sexenios) and the total PDI with teaching in the degree who may have research periods. Scale info: Percentage with two decimal places. |
83,33 |
|
IN25 Percentage of PhD teaching and research staff over total teaching and research staff. Percentage ratio between the doctoral PDI (Teaching and Research Staff) and the total PDI with teaching in the degree. Scale info: Percentage with two decimal places. |
100,0 |
|
IN26 Percentage of civil servant teaching and research staff over total teaching and research staff. Percentage ratio between the tenured PDI (Teaching and Research Staff) and the total PDI with teaching in the degree. Scale info: Percentage with two decimal places. |
62,5 |