The Master's Degree in Artificial Intelligence Research has a course load of 60 ECTS credits and is taught offsite.

The program can be completed full time in a single academic year or part time over two academic years. In case of the latter, students must be enrolled for a minimum of 30 ECTS credits and maximum of 45 credits the first year.

The master's is divided into 3 mandatory credits, 45 elective credits and 12 credits for the End of Master's Project. It includes three specialties:

• Specialty 1: Specialty in Learning and Data Science

• Specialty 2: Specialty in Web Intelligence

• Specialty 3: Specialty in Reasoning and Planning

The following is the structure for the studies plan based on specialty:

SPECIALTY 1 - LEARNING AND DATA SCIENCE

1. 102463 - A6. Introduction to research (3 ECTS credits)

2. Learning and data science (22,5 ECTS credits):

• 102470 - A7. Supervised methods (4,5 ECTS)

• 102471 - A8. Non-supervised methods and detecting anomalies (4,5 ECTS)

• 102472 - A9. Temporal and complex data (4,5 ECTS)

• 102473 - A10. Big data: Tools for processing bulk data (9 ECTS)

3. Students must choose elective subjects which include the specific competences C1, C3 and CE4 (22,5 elective credits).

You can consult this table for the relationship between subjects and the plan for corresponding competences.

4. 102484 - End of Master's Project (12 ECTS credits)

SPECIALTY 2 - WEB INTELLIGENCE

1. 102463 - A6. Introduction to research (3 ECTS credits)

2. Web intelligence (22,5 ECTS credits):

• 102474 - A11. Semantic web and linked data (4,5 ECTS)

• 102475 - A12. Advanced semantic technologies (4,5 ECTS)

• 102476 - A13. Recommendation systems (4,5 ECTS)

• 102477 - A14. Recovering and extracting information, graphs and social networks (4,5 ECTS)

• 102478 - A20. Empirical methods of natural language processing (4,5 ECTS)

3. Students must choose elective subjects which include the specific competence CE1(22,5 elective credits).

You can consult this table for the relationship between subjects and the plan for corresponding competences.

4. 102484 - End of Master's Project (12 ECTS credits)

SPECIALTY 3: REASONING AND PLANNING

1. 102463 - A6. Introduction to research (3 ECTS credits)

2. Reasoning and planning (22,58 ECTS credits):

• 102479 - A15. Automatic reasoning (4,5 ECTS)

• 102480 - A16. Automatic planning (4,5 ECTS)

• 102481 - A17. Advanced heuristics search (4,5 ECTS)

• 102482 - A18. Reasoning with restrictions (4,5 ECTS)

• 102483 - A21. Reinforcement Learning (4,5 ECTS)

3. Students must choose elective subjects which include the specific competence CE2 (22,5 elective credits).

You can consult this table for the relationship between subjects and the plan for corresponding competences.

4. 102484 - End of Master's Project (12 ECTS credits)

A1 - Virtual onsite sessions: initial viewing of audiovisual material (introductory videos, presentations, animation) included for each subject which serve as a presentation of each of the topics.

A2 - Individual work: completion of exercises, solving problems, practical exercises and/or individual projects.

A3 - Autonomous work: studying of basic material, complementary reading and other content for study.

A4 - Forums and chats: proposing questions and general discussion topics.

A5 - Tutorials: consults and resolving doubts, clarifications, etc.

Training activity "A2 - Individual work" will be exclusively practical in nature. It is with this training activity that students will mobilize knowledge and abilities presented in the virtual onsite sessions taking on problems with design, implementation, validation and applying the algorithms underlying the competences for the degree.

E1 - Evaluation questionnaires: students will complete an evaluation questionnaire for each teaching unit which will count towards the final score.

E2 - Evaluating forum and chat participation: the level of participation/debate by students will be evaluated and count towards the final grade.

E3 - Evaluation of individual work: the problems, projects and work done and submitted via the platform will be evaluated and support will be provided where necessary (especially regarding code development) via code management platforms like GitHub. There will also be a video that students must send to the professor for each subject.

E4 - Evaluation of End of Master's Project: the End of Master's Project written by the student will be evaluated along with its defense before a panel.

All of these activities will be supported by the online teaching platform which will also allow determining the identity of the students participating in the evaluation processes.

At the same time, at least once per subject, students will be asked to submit a video about one of the topics covered which will be evaluated by the professor for the subject.

The Master's Degree in Artificial Intelligence Research represents a modern and innovative proposal in both the field of postgraduate studies and artificial intelligence (AI).

This proposal arose from the ever-growing need to provide training on the most relevant and interesting paradigms in the field of AI and their application to problem-solving.

Interest in AI as a methodology and technology which gives intelligence to applications and systems is undeniable. More classical fields like knowledge-based systems are joined by other more recent fields such as the development of intelligent applications for mobile devices, automatic information search and processing and the growing need for the intelligent analysis of big data which set the scene for an incredible potential to produce highly valuable services and products.

We must remember that, according to the Broadband Commission report (2012), it is believed that in 2020 there will 25 billion connected devices (6 per "connected" person), all of which will generate and consume data and information. This growth potential is reflected at both the national and international level in the area of R&D+i as both national programs in Spain (government-sponsored programs for scientific research and technical research and those for R&D+i geared towards societal challenges) and international programs (especially the European Horizon 2020) include intelligent topics ("smart", "intelligent", etc.) as transversal methodologies and/or technologies in the complete practices of their work programs and funding instruments.

Thus, lines of research such as "intelligent and adaptive information management systems", "artificial cognitive systems", "smart, inclusive and sustainable growth", "smart integrated systems", "smart personalized assistive technologies", "intelligent transportation systems" and "smart building, cities and communities", to name a few, directly deal with the competences of a postgraduate student trained in artificial intelligence research. This degree should provide training in methodology to analyze, design, construct and verify solutions that, for example, analyze and process data to extract knowledge which serves as support to make decisions, make estimations and/or plan actions.

The master's is offered in collaboration with the Spanish Association for Artificial Intelligence (AEPIA), which assumes students will acquire unique competences due to the depth of the program thanks to the work by the team of professors whose academic and research qualifications are exceptional. This allows successful implementation for a number of both incoming and outgoing students at the national and international levels given the large international impact of the teaching staff for the program.

The program features additional interesting characteristics for a large number of students at the international level as it is taught in Spanish, with the desire to train students on different continents (notably Europe and America) and it represents a mechanism to access a variety of doctoral programs where a quality doctoral thesis can be completed. The fact that the master's is taught offsite (online) is key in this double objective typical of modern research to complete a master's and then go on to complete a doctoral thesis.

CG1 - Understand the concepts, methods and applications of artificial intelligence.

CG2 - Evaluate new computation and management tools to become familiar with the field of artificial intelligence.

CG3 - Intelligently manage data, information and its representation.

CG4 - Describe research problems through precisely writing the objectives to meet, the hypothesis to be used, the techniques to apply, the conjectures to formulate and the limitations to consider.

CG5 - Evaluate the proposed research hypothesis in a scientific work that allows for its validation or rebuttal.

CE1 - Use different search algorithms based on managing knowledge applied to the problems that come up in the field of artificial intelligence.

CE2 - Apply the automatic learning techniques used in validation methodology and present the most appropriate results in each case.

CE3 - Select the most appropriate mechanism to represent knowledge and reasoning methods for the context in which they will be used and design the application for problems in the field of artificial intelligence.

CE4 - Be familiar with the main reasoning models to evaluate their suitability to solve problems that come up in the field of artificial intelligence.

CE5 - Analyze documentary sources unique to the field of artificial intelligence research in order to determine which of them are relevant to solve specific problems.

Basic Competences

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

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

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

CB9 - That 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

CB10 - That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.

The Master's Degree in Artificial Intelligence Research is especially aimed towards university graduates with degrees in:

• Computer Engineering

• Degree in Mathematics

• Degree in Physics

• Industrial Engineering

• Engineering and Telecommunications

Applicants will also have to have demonstrated competence in one of the following programming languages: C, Java, Fortran, Python, Matlab or similar. This is an exclusionary admission criteria.

As part of the field of artificial intelligence and thanks to its format, this master's will suppose a unique access to postgraduate studies with the possibility to be a major reference in the near future.

Thanks to its content, the master's will facilitate work placement for students in an area with major real applications and a variety of innovative possibilities to transfer companies.

At the same time, the program includes deep scientific and technical content which reinforces the research aim both to later complete the doctoral thesis (perhaps related to the End of Master's Project) and to join public teaching and research bodies or private companies with R&D+i departments.

Globally speaking, this master's and its faculty offer accumulated experience and the positive intention of training human resources to be able to access doctoral programs as well as an aspect of realism (useful for the modern industrial fabric) and explicit knowledge in the lines of work at the Spanish level (generating knowledge and challenges), the European level (H2020 and related programs) and the international level (connections with the NSF, Japan, China, etc.).

Access with an official university degree from Spain or the European Higher Education Area (EHEA): Admission to the Master's Degree in Artificial Intelligence Research requires an official university degree from Spain or another higher education institution belonging to another Member State of the EHEA which grants access to study a master's degree in the country of origin. Special preference will be given to the following degrees: Computer Engineering, Degree in Mathematics, Degree in Physics, Industrial Engineering and Engineering and Telecommunications.

Applicants will also have to have demonstrated competence in one of the following programming languages: C, Java, Fortran, Python, Matlab or similar. This is an exclusionary admission criteria.

Access with a university degree from outside the EHEA: Students with degrees from education systems outside the EHEA can be accepted without requiring official recognition of their degrees by accrediting that the level of studies is the equivalent to that of official university degrees in Spain and that the degree allows access to postgraduate studies in the country of origin. Access for these students depends on a favorable decision from the Rector.

The decision by the Rector will never imply official recognition of the degree the student possesses nor its recognition for purposes other than to study the master's degree.

Applications for admission must be sent via the web site "Online Pre-Registration" which can be accessed on the UIMP website at http://www.uimp.es/preins/index.php.

Students currently enrolled in the last year of their university degree who are due to present and defend their End of Degree Project can pre-register. Admission will depend, in this case, on meeting the admission requirements and the accreditation of the degree granting access during the registration period.

Students must attach the following required documentation in PDF or JPG format in the section labeled "Required Documentation" along with the online registration form:

DOCUMENTATION REQUIRED FOR THE APPLICATION PROCESS

1. Legalized photocopy of ID document (in the case of Spanish students) or NIE or passport (in the case of foreign students).

2. Legalized photocopy of degree which grants access to the master's degree, or proof of paying the fees to issue the degree.

3. Personal academic transcript (or legalized photocopy).

4. ID-size photo, in JPG format, identifying the file with the student’s surname(s) and name, without spaces.

5. Résumé (maximum of 4 pages), in PDF format which allows evaluating other suitable merits from the applicant's profile: demonstrated competence in programming languages (C, Java, Fortran, Python, Matlab or similar); certified level of English (B1 or B2); other merits related to artificial intelligence (professional experience, other degrees, publications).

Students with a non-homologated degree or one which is in the process of homologation must additionally provide:

6. Certification from the university where studies were completed which states that the degree grants access to postgraduate studies in the country where it was issued.

7. Personal academic transcript which states the official length of the program in academic years, the curriculum followed, the subjects studied, grades received and the course load for each of them.

NOTE: Students with a foreign degree which has not been homologated or is in the process of homologation must present the legalized documents translated into Spanish as necessary.

The required original documentation must ONLY be presented at the Student Secretary (C/ Isaac Peral 23. 28040 Madrid, Spain) in the case the student's application has been accepted by the Academic Committee for the master's.

STUDENTS WITH SPECIFIC EDUCATIONAL NEEDS

Universal accessibility is guaranteed and supervision will ensure that students with a disability have the necessary resources and support to correctly complete the master's by asking them to indicate their specific needs when applying for the program.

In this link you can consult the UIMP Protocol for the attention of students with specific educational needs.

The following will be considered in the admission process:

1) Suitability of degree granting access (up to 50 points):

• Computer Engineering: 50 points

• Degree in Physics, Mathematics, Industrial Engineering, Telecommunications or similar: up to 40 points

• Other related Degrees: up to 35 points

2) Competence in working with and using one of the following programming languages: C, Java, Fortran, Python, Matlab or similar (up to 25 points):

• Expert: 25 points

• Intermediate: 10 points

• Beginner: 2 points

3) Certified level of English (up to 10 points):

• B1: 5 points

• B2: 10 points

4) Other merits related to artificial intelligence (up to 15 points):

• Professional experience

• Other degrees

• Publications

The Academic Committee for the Master's will be in charge of examining and valuing admission applications and approving the accepted candidates according to the previously mentioned criteria. The Academic Committee will publish an acceptance list each academic course on the UIMP web site.

Students will have specific advisors for each block of subjects. Advisors will be available to students to guide and support them throughout the program. These advisors are members of the Academic Committee for the master's and will advise the student on subjects in their area of competence. The following are the block advisors:

• Luis Magdalena Layos: Foundations of Artificial Intelligence

• Eva Onaindía: Reasoning and planning

• Alicia Troncoso: Learning and data science

• Óscar Luaces: Web intelligence

Additionally, the advisors listed for each subject will provide academic guidance to complete individual and group projects, resolve doubts and complete continuous evaluation tasks. Each student will also have an advisor who will guide them in completing the End of Master’s Project.

The Virtual Campus used for the master's to develop the teaching-learning process includes different tools that allow advisors and professors to establish a very efficient supervising and support procedure for students virtually in real time.

The Virtual Campus offers powerful tools to consult student development both personally and as a group, offering detailed information about this monitoring: control tools to measure user participation, use, motivation, etc.; evaluation tools to analyze development in the learning process; and alerts systems that inform users of pending tasks.

Additionally, the following are organized procedures to support students:

• Email: Students will be able to communicate with advisors and professors for each subject using this means in order to resolve academic doubts and ask for specific types of guidance.

• Forums: Forums will be a means for student participation in all academic aspects. Advisors and professors for each subject will intervene in the forums along with students to moderate forums and provide precise information both about specific subjects and the organization of the master's.

• Video interviews: Students can arrange video interviews with their professors and advisors. This direct means of communication will serve to resolve more personal matters students have regarding the master's.

• Chats: Students can establish asynchronous communication via chats with their professors and advisors. This means will complement those previously mentioned and will serve to arrange video interviews and resolve short doubts, for example.

Eva Onaindia de la Rivaherrera, Department Chair of Computer Languages and Systems, Universidad Politécnica de Valencia

Contact email: master@aepia.org

The faculty for the master's consists of professors from 21 universities and research centers.

Detailed information about professors associated with the program can be consulted here and on the "Syllabus" section on this web page.

Each module has a professor who is in charge of coordinating the different lessons which make up each specialty.

Coordinating professors for specialties

• Learning and data science:

Beatriz Remeseiro López. Ph.D Assistant Professor of Computational Science and Artificial Intelligence, Universidad de Oviedo

• Web intelligence:

Luis Magdalena Layos. Professor of Computational Science and Artificial Intelligence, Universidad Politécnica de Madrid

• Reasoning and planning:

Eva Onaindía de la Rivaherrera. Department Chair of Computer Languages and Systems, Universidad Politécnica de Valencia

• Coordination with students:

José M.ª Luna Ariza. Ph.D Assistant Professor of Computational Science and Artificial Intelligence, Universidad de Jaén

Beatriz Remeseiro López. Ph.D Assistant Professor of Computational Science and Artificial Intelligence, Universidad de Oviedo