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Home   >  Master's & postgraduate courses  >  Education  >  Postgraduate course in Artificial Intelligence with Deep Learning
We advise you! Request information or admission
  • Presentation
  • Training Content
  • Learning methodology
  • Final projects
  • Teaching team
  • Associates entities
  • Career opportunities
  • Testimonials
  • Request information or admission

Presentation

Edition
6th Edition
Credits
15 ECTS (120 teaching hours)
Delivery
Online
Language of instruction
English
Fee
€4,100
Special conditions on payment of enrolment fee and 0,7% campaign
Registration open until the beginning of the course or until end of vacancies.
Start date
Start date: October of 2025
End date: March of 2026
Timetable
Monday: 6:00 pm to 8:00 pm
Wednesday: 6:00 pm to 8:00 pm
Taught at
Online
Presentation video
Why this postgraduate course?

Artificial intelligence (AI) is at the core of the industrial revolution 4.0, and its influence in society and economy is every year more pronounced. The availability of large volumes of data and computational resources with affordable costs has made possible, over the last decade, the training of deep neural networks, a powerful tool in machine learning. Multiple companies are already applying this data-driven programming paradigm, while in parallel public administrations are also developing strategic plans to lead the sector. Progress has accelerated in 2023, with systems like GPT-4, Gemini and Claude 3, impressively multimodal. Companies are racing to build AI-based products, and AI is increasingly being used by the general public.

According to the AI Index from Stanford University, in 2023, global corporate investment in AI was over $189B, with a thirteenfold increase over the past decade. Funding for generative AI has surged, nearly octupling from 2022 to reach $25.2 billion. The number of newly funded AI companies in 2023 was 1.812, a 40.6% increase over the previous years. This has resulted in a significant increase of job postings across every sector. In the US, AI-related job postings made 1.6% of all job postings. In Spain, it made 1.4%, while it was 0.4%, in 2018, and the amount of hiring has doubled compared to its average during the 2015-2016 period. These positions require knowledge on natural language processing, computer vision and robotics, applications that have recently experienced great advances thanks to deep learning. Public investment in AI is growing as well. The EU Digital Europe programme will fund AI with a total of €2.1 billion in the 2021-2027 period. This context explains why the job analysis portal glassdoor.com has chosen data scientist and machine learning engineers among the best jobs in the United States during the last years, being the skills in deep learning the most demanded.

The postgraduate course in Artificial Intelligence with Deep Learning aims to satisfy this demand of professionals thanks to an experienced teaching team with world-class reputation in both industry and academia. Course instructors develop deep learning-powered systems for many customers, and also lead ground-breaking research with regular publications in top scientific venues such as the Conference on Neural Information Processing Systems (NeurIPS), the Conference on Computer Vision and Pattern Recognition (CVPR), and the International Conference on Learning Representations (ICLR). With their support, the students in our program become proficient in both the PyTorch software framework for deep learning, and the theoretical basis necessary to understand the opportunities and limitations.

Promoted by:
  • Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona. ETSETB (UPC)
Aims
  • Design deep learning models, especially for processing text, images, video and audio.
  • Optimize and monitor the training of deep neural networks.
  • Process large data volumes with specialized hardware: Central Processing Unit (CPU) and Graphics Processing Unit (GPU).
  • Implement solution in deep learning frameworks.
  • Develop projects powered by artificial intelligence.
Who is it for?
  • Graduates in telecommunications, computer science, math and physics who would like to develop their skills on machine learning with deep neural networks.
  • IT professionals working who would like to focus their activity towards artificial intelligence.
  • Software developers willing to benefit from the new opportunities created by artificial intelligence.

Students must have high-speed Internet access in order to access live video conferencing, and a computer with the Google Chrome browser. No special hardware or software is required for the computer.

Training Content

List of subjects
4 ECTS 31h
Deep Learning
  • Introduction to machine learning.
  • Backpropagation training.
  • The perceptron.
  • Softmax and Multilayer perceptron.
  • Losses.
  • Convolutional Neural Networks (CNN).
  • Interpretability.
  • Optimization.
  • Methodology.
  • Graph convolutional networks and Recommender Systems.
3 ECTS 24h
Natural Language Processing
  • Recurrent neural networks (RNN).
  • Attention.
  • Transformers.
  • Introduction and text processing.
  • Word embeddings.
  • Language models and advanced adaptations.
3 ECTS 23h
Computer Vision
  • Transfer learning.
  • Self-supervised learning and autoregressive models.
  • Metrics and recovery.
  • Video architectures.
  • Object detection.
  • Segmentation.
  • Variational autoencoders (VAE).
  • Generative adversarial networks (GAN) and diffusion.
2 ECTS 15h
Advanced Applications
Students will be able to decide the itinerary of the subject, choosing one option from block A and one option from block B.

Block A (6 teaching hours)
  • Option 1: Advanced NLP
    • Advanced applications.
    • Advanced personalisation and training techniques.
  • Option 2: Advanced CV
    • 3D reconstruction.
    • Anomaly detection with VAE.
    • Applications of generative models.
    • Video.
Block B (9 teaching hours)
  • Option 1: Speech Processing
    • Introduction to audio and speech.
    • Speech enhancement.
    • Speech recognition.
    • Text-to-speech.
  • Option 2: Reinforcement Learning
    • Introduction to Reinforcement Learning.
    • Tabular Q-Learning.
    • Deep Q-Learning.
    • Policy gradient.
3 ECTS 27h
Project
  • Programming in Python for deep learning and setup.
  • Hyperparameters.
  • Cloud computing.
  • APIs.
  • Monitoring of neural network training: training curves, computational resources.
  • Docker.
The projects will be executed in groups of 4 students.
The UPC School reserves the right to modify the contents of the programme, which may vary in order to better accommodate the course objectives.
Degree
Postgraduate qualification in Artificial Intelligence with Deep Learning, issued by the Universitat Politècnica de Catalunya. Issued by virtue of the provisions of art. 7.1 of Organic Law 2/2023 of 22 March, concerning the University System, and art. 36 of Royal Decree 822/2021 of 28 September, which establishes the organisation of university education and the procedure for ensuring its quality. A prior official university qualification is necessary to obtain it. Otherwise, the student will receive a certificate of completion of the programme issued by the Fundació Politècnica de Catalunya. Lifelong learning studies at the Universitat Politècnica de Catalunya are approved by the University's Governing Council on an annual basis. (See details appearing on the certificate).

Learning methodology

The teaching methodology of the programme facilitates the student's learning and the achievement of the necessary competences.

The learning methodology of the programme combines live (70%) and recorded (30%) content. This scheme prioritizes the online interaction between instructors and students, but also exploits the flexibility of schedules allowed by pre-recorded video.

There exist two types of sessions: practical and lecture sessions. Practical sessions are based on a live development and coding of a practical case that students build in synchronization with the instructor, who will address their questions. Lecture sessions are built on top of a recorded talk that students watch previously at their convenience. During the lecture session, the instructors will review the contents of the talk and slides, solve questions from students, and propose exercises to consolidate the learning goals.

All students must have high speed Internet access for accessing the live video-lectures and a computer with a modern web browser.



Learning tools
Participatory lectures
A presentation of the conceptual foundations of the content to be taught, promoting interaction with the students to guide them in their learning of the different contents and the development of the established competences.
Practical classroom sessions
Knowledge is applied to a real or hypothetical environment, where specific aspects are identified and worked on to facilitate understanding, with the support from teaching staff.
Solving exercises
Solutions are worked on by practising routines, applying formulas and algorithms, and procedures are followed for transforming the available information and interpreting the results.
Flipped classroom
The contents are prepared prior to the face-to-face lessons. Practical sessions take place in the classroom, which enable understanding and application of concepts to real cases and the expansion of knowledge with more technical and specialised details.
Tutorship
Students are given technical support in the preparation of the final project, according to their specialisation and the subject matter of the project.
Assessment criteria
Attendance
At least 80% attendance of teaching hours is required.
Level of participation
The student's active contribution to the various activities offered by the teaching team is assessed.
Solving exercises, questionnaires or exams
Individual tests aimed at assessing the degree of learning and the acquisition of competences.
Completion and presentation of the final project
Individual or group projects in which the contents taught in the programme are applied. The project can be based on real cases and include the identification of a problem, the design of the solution, its implementation or a business plan. The project will be presented and defended in public.
Work placements & employment service
Students can access job offers in their field of specialisation on the My_Tech_Space virtual campus. Applications made from this site will be treated confidentially. Hundreds of offers of the UPC School of Professional & Executive Development employment service appear annually. The offers range from formal contracts to work placement agreements.
Virtual campus
The students on this postgraduate course will have access to the My_ Tech_Space virtual campus - an effective platform for work and communication between the course's students, lecturers, directors and coordinators. My_Tech_Space provides the documentation for each training session before it starts, and enables students to work as a team, consult lecturers, check notes, etc.

Postgraduate's final projects

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Teaching team

Academic management
  • Pueyo Morillo, Jorge
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    PhD student in Computer Vision at the Polytechnic University of Catalonia (UPC). Master in Advanced Telecommunication Technologies with Deep Learning Specialization by the UPC. Degree in Telecommunication Technologies and Services Engineering from the Higher Technical School of Telecommunications Engineering of Barcelona (ETSETB). Currently doing research in the field of Computer Vision, especially applied to 3D content. Previously part of the Mobile Wireless Internet group of the i2cat research center.
  • Ruiz Hidalgo, Javier
    Ruiz Hidalgo, Javier
    info
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    The holder of a doctorate in Telecommunications Engineering from the Universitat Politècnica de Catalunya (UPC) and a MSc degree by the University of East Anglia (UEA), UK. Associate Professor in the Department of Signal Theory and Communications at UPC, and a member of the Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC). He has led research and technology transfer projects in the field of computer vision - area in which he publishes internationally. His research focuses on deep learning and applications in 3D graph processing and generative networks.
Teaching staff
  • Aguilar Carrillo, Rafael Ignacio
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    Computer Engineer from the Lisandro Alvarado Central Western University (UCLA). He currently works as a software engineer in the GlovoMaps team at Glovo. He is a software engineering mentor for organizations and individuals. He has more than ten years of experience in different transnational companies in fields such as logistics, retail, real estate and software consultancies.
  • Albors Zumel, Laia
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    Graduated in Data Science and Engineering from the Universitat Politècnica de Catalunya (UPC), and holds a master's degree in Computer Vision from the Universitat Autònoma de Barcelona (UAB). She is currently doing her doctorate in the Department of Signal Theory and Communications at the UPC, and is writing her doctoral thesis on the efficient use of deep learning techniques for the detection and identification of fauna species and flora. She previously worked at the Barcelona Supercomputing Center (BSC), in the Emerging Technologies for Artificial Intelligence group in a joint project with CaixaBank.
  • Anglada Rotger, David
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    PhD candidate in Medical Image Processing from the Polytechnic University of Catalonia (UPC). Master in Advanced Mathematics and Mathematical Engineering from the UPC. Graduated in Mathematics and Data Science and Engineering from the Interdisciplinary Training Center (CFIS) by the UPC. Currently, a research assistant in the Digipatics project, for the development of artificial intelligence algorithms for the processing of histopathological images, in collaboration with the Catalan Institute of Health (ICS).
  • Cámbara Ruiz, Guillermo
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    Graduated in Physics from the University of Barcelona. He is a doctoral student in automatic speech recognition at Pompeu Fabra University (UPF) and Telefónica Research, and has a master's degree in Interactive Intelligent Systems from UPF. His research in deep learning for audio processing, speech and natural language has been applied in cognitive systems including Aura, Telefónica's home assistant, and Ingenious, a voice-to-voice translator for European emergency teams. He has also worked with researchers at prestigious institutions, such as the Brno University of Technology (BUT) and Dolby Labs.
  • Cardoso Duarte, Amanda
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    A PhD candidate and Marie Skodowska-Curie fellow at the Barcelona Supercomputing Center and UPC, supported by the "La Caixa" Foundation through the INPhINIT - 'La Caixa' Doctoral Fellowship programme. She graduated in Systems Analysis from Sul-Rio-Grandense Federal Institute of Education, Science and Technology in Brazil, and obtained her master's degree in Computer Engineering at Federal University of Rio Grande. During her Ph.D. programme, she was a visiting student at John Hopkins University (2018) and at Carnegie Mellon University (2019). Her research interests focus on combining Accessibility, Human-Computer Interaction, and Applied Machine Learning.
  • Carós Roca, Mariona
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    Holder of a master's degree in Telecommunications Engineering from the Polytechnic University of Catalonia (UPC), specialising in multimedia (DL in vision, speech and text). She worked at Telefónica as a Data Scientist developing DL models to detect anomalies in networks. She is currently taking her doctorate in LiDAR data modeling for environmental applications at the University of Barcelona (UB), in collaboration with the Cartographic and Geological Institute of Catalonia (ICGC). She is also a member of Young IT Girls, a non-profit organisation encouraging girls to pursue technology studies.
  • Carrino, Casimiro Pio
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    Degree in Physics from the University of Naples Federico II and Master in Physics of Complex Systems from the University of Turin. He has 8 years of experience as a researcher in Natural Language Processing (NLP). He is a former member of the Barcelona Supercomputing Center (BSC), where he developed Large Language Models for Catalan and Spanish languages. Now, he is a senior research fellow at Avature working on generative AI and information retrieval for the labour market. Concurrently, he's pursuing a PhD at the Universitat Politècnica de Catalunya (UPC), exploring deep learning's applications in automatic multilingual question answering.
  • Caselles Rico, Pol
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    A graduate in Telecommunications Engineering and the holder of a master’s degree in Advanced Telecommunication Technologies from the UPC He is currently a doctoral student at the UPC, and works with the Institut de Robòtica Industrial (IRI) research centre. He works on 3D reconstruction with deep learning at Crisalix Labs. His bachelor's degree final project, which he wrote at the Insight Centre for Data Analytics (Dublin), focused on saliency prediction, and he wrote his master's degree final project on model weight disentanglement at the University of St. Gallen in Switzerland.
  • Escolano Peinado, Carlos
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    Doctor in computer science from Universitat Politècnica de Catalunya (UPC) and master's degree in Artificial Intelligence from the UPC. He is currently a researcher at the UPC's Department of Signal Theory and Communications and at the Barcelona Supercomputing Center (BSC), as well as an associate professor at the UPC's Department of Computer Science. His area of expertise is natural language processing, especially multilingual machine translation with neural networks.
  • Fojo Àlvarez, Daniel
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    He graduated in Mathematics and Physical Engineering from the Barcelona Interdisciplinary Higher Education Centre (CFIS) and holds a Master’s Degree in Advanced Mathematics and Mathematical Engineering. Machine learning engineer at Lace Lithography.
  • Gállego Olsina, Gerard Ion
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    The holder of a master's degree in Advanced Telecommunication Technologies from the Universitat Politècnica de Catalunya (UPC), specialising in Deep Learning for Multimedia Processing. He is currently a doctoral candidate in Automatic Voice Translation in the Department of Signal Theory and Communications at the UPC. He has carried out research stays at multinational companies (Apple, Amazon and Dolby), where he has worked on Large Language Models (LLM) and speech processing.
  • Giardina, Claudia
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    Master's degree in Computer Science from the Polytechnic Faculty of the National University of Asunción, Paraguay (UNA). The holder of a degree in Medical Electronics Engineering from the Polytechnic Faculty of the UNA. A specialist in Didactics in Higher Education at UNA. She is currently a doctoral student in the Department of Signal Theory and Communications at the Universitat Politècnica de Catalunya (UPC), working on a project involving artificial intelligence applied to medical imaging.
  • Giró Nieto, Xavier
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    An applied scientist at Amazon Science Barcelona, in the field of deep learning applied to computer vision. He was the founder and director of the postgraduate course in Artificial Intelligence with Deep Learning for the first nine courses between 2019-2022, which he combined with his research and teaching at the Universitat Politècnica de Catalunya (UPC) and the Institute of Robotics and Industrial Informatics (IRI). He is a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and one of the instigators of the Deep Learning Barcelona Symposium (DLBCN).
  • Gómez Duran, Paula
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    The holder of a master's degree in Advanced Telecommunication Technologies (MATT) from the Universitat Politècnica de Catalunya (UPC). She is currently taking a doctorate in Contextual Recommendation Systems at the University of Barcelona (UB). She has three years of experience in full-stack programming (Visual Engineering) and research in various fields of artificial intelligence, at universities including the University of Barcelona and the UPC, and at institutions including the Insight SFI Research Centre for Data Analytics, Telefonica Research and TV3. She has recently published a study on Graph Convolutional Embeddings for Recommender Systems.
  • Granero Moya, Marcel
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    PhD Candidate in Artificial Intelligence at UPF Barcelona. Ex-Amazon AI Cambridge. Master in Data Science at EPFL Switzerland. Bachelor's in Telecommunications Engineering at UPC BarcelonaTech.
  • Hernández Pérez, Carlos
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    Doctoral Ph.D. student at Universitat Politècnica de Catalunya (UPC). He has a deep interest in A.I. technology and how it can benefit the future of our humanity. He focuses on its use for medical applications, but also enjoys using it for artistic purposes.
  • Jiménez Martín, Lauren
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    A doctoral student in the Department of Signal Theory and Communications at the Universitat Politècnica de Catalunya (UPC), funded by FI AGAUR 2022. The holder of a bachelor's degree in Computer Science from the University of Havana. She has applied machine learning techniques to restore medical images. She is currently preparing her doctoral thesis on the application of deep learning to solve medical problems in histopathological images, and the study of Attention and Transformers in particular.
  • Malik Ara, Ibrar
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    Graduated in Data Science and Engineering from the Universitat Politècnica de Catalunya (UPC), and holding a master's degree in Computer Vision from the Universitat Autònoma de Barcelona (UAB). Currently pursuing a PhD at the IRI (CSIC-UPC). His research focuses on the 3D reconstruction of humans. Professionally, he serves as a deep learning engineer at Crisalix, applying his expertise to innovate in the field of 3D reconstruction and deep learning in production.
  • Nieto Salas, Juan José
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    Bachelor's degree in Telecommunications Engineering from the Universitat Politècnica de Catalunya (UPC) and a master's degree in Data Science from the UPC. He did a research assistant internship using deep learning and reinforcement learning techniques at the Insight Centre for Data Analytics and at Telefónica. He currently works as a Data Scientist at Glovo.
  • Pina Benages, Oscar
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    A doctoral student at the Universitat Politècnica de Catalunya (UPC). He holds a master's degree in Advanced Telecommunication Technologies, with a mention in Deep Learning for Multimedia Processing. His research focuses on self-supervised graph representation learning and its applications in medical image processing, and specifically in the field of digital histopathology.
  • Pons Puig, Jordi
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    A graduate in Telecommunications Engineering from the UPC, and holds a doctorate in Music Technology, Large Sound Collections and Deep Learning from the Music Technology Group at Pompeu Fabra University (UPF). He also has a master's degree in Sound and Music Technologies. He is currently a researcher at Dolby Laboratories. He did work placements at the Institut de Recherche et Coordination Acoustique/Musique de Paris (IRCAM), at the German Hearing Center in Hannover, at Pandora Radio and at Telefónica Research.
  • Pueyo Morillo, Jorge
    info
    View profile in Linkedin View profile in Orcid View profile in Google Scholar
    PhD student in Computer Vision at the Polytechnic University of Catalonia (UPC). Master in Advanced Telecommunication Technologies with Deep Learning Specialization by the UPC. Degree in Telecommunication Technologies and Services Engineering from the Higher Technical School of Telecommunications Engineering of Barcelona (ETSETB). Currently doing research in the field of Computer Vision, especially applied to 3D content. Previously part of the Mobile Wireless Internet group of the i2cat research center.
  • Ruiz Hidalgo, Javier
    info
    View profile in futur.upc / View profile in Linkedin
    The holder of a doctorate in Telecommunications Engineering from the Universitat Politècnica de Catalunya (UPC) and a MSc degree by the University of East Anglia (UEA), UK. Associate Professor in the Department of Signal Theory and Communications at UPC, and a member of the Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC). He has led research and technology transfer projects in the field of computer vision - area in which he publishes internationally. His research focuses on deep learning and applications in 3D graph processing and generative networks.
  • Sanchez Cervera, Ariadna
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    Bachelor's degree in Audiovisual Systems Engineering from the Universitat Politècnica de Catalunya (UPC) and The holder of a master's degree in Speech and Language Processing from the University of Edinburgh. Until 2023, she was a researcher on Amazon's text-to-speech team. She is currently completing a PhD in Speech and Voice Technologies for Pathological Voices at the University of Edinburgh.
  • Tarrés Benet, Laia
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    A graduate in Telecommunications Engineering from the Universitat Politècnica de Catalunya (UPC), and the holder of a master's degree in Advanced Telecommunication Technologies from the UPC. She has participated in many deep learning projects with the Image Processing Group at the UPC. She is currently doing her doctorate at the UPC, and is preparing her doctoral thesis on the application of transformations in sign language. She has previously been involved in projects consisting of detecting skin lesions and colouring historical images in black and white using deep learning. He has also done internships at Amazon Research Germany.
  • Vilaplana Besler, Verónica
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    Holds a doctorate in Image Analysis from the Universitat Politècnica de Catalunya (UPC), a MSc degree in Mathematics and a MSc degree in Computer Sciences from the Universidad de Buenos Aires (Argentina). Associate professor at the Department of Signal Theory and Communications at UPC, teaching Deep Learning, Machine Learning and Computer Vision. Member of the Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC). Her research focuses on machine learning, deep learning and applications in medical imaging and remote sensing.
  • Ysern García, Maria
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    Master in Advanced Telecommunication Technologies (MATT) by the Universitat Politècnica de Catalunya (UPC), mention in Deep Learning for Multimedia Processing. Currently, a PhD student in the Department of Signal Theory and Communications at the UPC. Her research focuses on the use of generative models for medical imaging.

Associates entities

Collaborating partners

Career opportunities

  • Artificial intelligence engineer.
  • Engineer in deep neural networks.
  • Computer vision engineer.
  • Engineer in natural language processing.
  • Engineer in the processing of audio and voice.
  • Data analyst/data scientist.



Testimonials

Testimonials

I was looking for training to go more deeply into the area of deep learning and to be able to enter the labour market. My starting point was a completely theoretical profile, as my background is in mathematics. From the postgraduate degree in Artificial Intelligence with Deep Learning, I would highlight on the one hand its practical approach, and on the other, the wide range of content it covers. The course also works on both classic and modern developments of some ideas. This training has opened up a field with new opportunities for me, since this area has considerable impact in the current situation. The final project was very interesting. It was about the segmentation of medical images. The truth is that when I started the postgraduate course I couldn't imagine being able to do something that was that complex. In short, I would recommend this training because of its applied approach focused on the world of work, in which you learn the mechanics behind deep learning, and acquire the tools you need to put it into practice.

Núria Sánchez Alumni of the postgraduate course in Artificial Intelligence with Deep Learning

Testimonials
Artificial Intelligence is one of the latest technological topics, in and out of the professional world. As well as being personally interested in it, as a member of the digitisation team of an industrial company, I have to keep up with the times. If I can also get detailed technical knowledge, this is great added value both for the company I work for, and for my personal professional project. This is precisely what the postgraduate in Deep Learning brought me: a first immersion in this field of Artificial Intelligence, and the possibility of going further into its different areas, depending on my interest. The fact that the students included professionals from different sectors gave me new points of view, especially when identifying potential projects in which to apply AI. With the knowledge I gained, I have the information to promote the use of the technology within the company to optimise processes and even devise new business paths.

Martí Pomés Technical Lead of Process Robotics Projects in Omya

Testimonials
From my position at CatSalut doing data analysis in public health, I wanted to learn more about how to apply statistics to obtain valuable information from large amounts of data, especially to help medical diagnosis. The postgraduate degree allowed me to solidly understand the bases of deep learning and the different branches in which it can be applied. It has a very practical aspect that allows you to read ready-made programs, modify them and create your own. The highly specialized teachers, together with the possibility of carrying out a deep learning project from scratch, contribute to achieving visible and real results. What I learned, I have been able to apply in my professional career. In fact, I have been so encouraged that I will start a doctorate in this field, where I will apply artificial intelligence to generate medical images.

Júlia Folguera Data Analyst at CatSalut

Testimonials

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Cancellation or deferment.The Fundació Politècnica de Catalunya reserves the right to cancel or defer a course if the minimum number of students is not met. In case of cancellation or non-admittance, the Fundació Politècnica de Catalunya will return all amounts paid in full, without any additional compensation. In case of deferment, applicants may request reimbursement of fees paid.

Cancellation of registration.
In the event of withdrawal or cancellation of the registration, the student must notify the UPC School in writing beforehand.
  • If this request for cancellation is made 45 days before the start of the programme, the UPC School will retain only 30% of the total registration fee and refund the difference paid.
  • In the event that the application request is made within 45 calendar days and the beginning of the programme, the UPC School will retain 60% of the registration fee.
  • No applications for refunds may be made after the programme has started.
Under exceptional circumstances, refunds of the registration fee will be made if the student's cancellation is due to one of the following circumstances:
  • Denial of a visa, subject to submission of supporting documentation. In this case, the UPC School will refund the registration fee less 300 Euros for administrative expenses.
  • Serious illness or accident accredited by an official medical certificate, stating the start date of the illness and the anticipated convalescence period. In this situation the UPC School's decision will be as follows:
    • If the notification takes place up to one month after the start of the programme, it will refund the amount actually paid, less 300 Euros as administrative expenses.
    • No refunds will be made after a month after the start of the programme. It will be only be possible to use the amount paid as a deposit for the registration fee of the next programme. This procedure entails no administrative fee for the student. The price difference between the new registration fee and the amount previously paid will be payable by the student under all circumstances.

Changes in registration. Any changes in registration, previously authorised by the Fundació Politècnica de Catalunya, will incur a 300 € administration fee.

Discounts.
  • Discounts are non-accumulable. The greater discount of those requested will be applied.
  • Discounts can only be applied under prior application and approval.
  • Once registration has been confirmed, no discount will be applied.
  • Students are responsible for placing applications for any discounts.

Qualification. In order to obtain the Qualification/Diploma issued by the Polytechnic University of Catalonia, the student must be in possession of a recognised university qualification or internal university qualification equivalent to a degree or diploma. If this is not the case, the student will receive a certificate of completion for the course, issued by the Fundació Politècnica de Catalunya. Students with outstanding payments due to the Fundació Politècnica de Catalunya or who has not approved all the credits necessary to overcome the program before the date of completion of this program will not be eligible to receive any qualification, diploma or certificate.

Barcelona, October 31, 2017


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