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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

Edition
7th
Credits
15 ECTS
Delivery method
Live online
Language of instruction
English
Fee
€4,100
Special conditions on payment of enrolment fee and 0,7% campaign

Finance your registration with Sabadell Consumer Sabadell Consumer Simulator and submit your application through your Program Advisor:

Isabel de la Fuente Larriba
(34) 93 115 57 51
isabel.delafuente@upcschool.upc.edu

">
. Use the simulator to easily calculate your fees and request it through your Program Advisor.

Registration open until the beginning of the programme or until end of vacancies.
Dates
Start date: 05/10/2026
End date: 17/03/2027
Class schedule
Monday: 6:00 pm to 9:00 pm
Wednesday: 6:00 pm to 9:00 pm
Presentation video
Why this programme?

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.
Knowledge of Python programming is required, as well as a basic understanding of algebra and calculus at the engineering undergraduate level. Students must have access to high-speed internet to attend live video lectures and a computer with the Google Chrome browser. The computer does not require any special hardware or software.

Training Content

List of subjects
4 ECTS 30h
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 21h
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 21h
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 programme objectives.
Degree
University-specific Expert Diploma in Artificial Intelligence with Deep Learning, Issued by the Universitat Politècnica de Catalunya. Issued in accordance with the provisions of Article 7.1 of Organic Law 2/2023, of 22 March, on the University System; Article 36 of Royal Decree 822/2021, of 28 September, which establishes the organisation of university education and the procedure for quality assurance; and Articles 2 and 8 of the Amendment to the Regulations for Continuing Education Programmes, approved by Agreement CG/2025/02/35, of 25 March, of the Governing Council of the UPC. To obtain this qualification, it is necessary to hold a prior university degree equivalent to level 2 of the Spanish Qualifications Framework for Higher Education (MECES). Otherwise, the student will receive a certificate of completion of the programme issued by the Fundació Politècnica de Catalunya. (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 programme'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.

Teaching team

Academic management
  • Ruiz Hidalgo, Javier
    Ruiz Hidalgo, Javier
    info
    / / /
    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.
  • Pueyo Morillo, Jorge
    info
    / / /
    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.
Teaching staff
  • Aguilar Carrillo, Rafael Ignacio
    info

    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
    info

    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
    info

    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
    info

    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
    info

    PhD in Signal Theory and Communications from the Universitat Politècnica de Catalunya (UPC). Master's degree in Computer Engineering from Universidade Federal do Rio Grande (FURG - Brazil). Currently an AI4S Fellow and Artificial Intelligence Team Leader at the Barcelona Supercomputing Center (BSC), leading projects that integrate AI/ML in Earth science tasks. With a background in multimodal learning, sign language translation, and climate-related AI applications, she has contributed to major European research initiatives such as Destination Earth and Horizon Europe projects.
  • Carós Roca, Mariona
    info

    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
    info

    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
    info

    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
    info
    /
    Doctor in Computer Science from the Universitat Politècnica de Catalunya (UPC) and a master's degree in Artificial Intelligence from the UPC. He is currently a researcher in the language technologies group at the Barcelona Supercomputing Center (BSC), as well as an associate professor in the Department of Computer Science at the UPC. His area of expertise is natural language processing, especially multilingual machine translation with neural networks.
  • Fojo Àlvarez, Daniel
    info

    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.
  • Giardina, Claudia
    info
    /
    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
    info
    / / /
    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
    info

    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
    info

    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
    info
    /
    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
    info

    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
    info

    Graduated in Data Science and Engineering from the Polytechnic University of Catalonia (UPC) and a Master in Computer Vision from the Autonomous University of Barcelona (UAB). He currently works in the deep learning team at Crisalix as a tech lead, applying his experience in the field of 3D reconstruction and deep learning in production.
  • Mosella Montoro, Albert
    info

    Research Scientist specializing in the intersection of Computer Vision and Graphics. He is currently collaborating with the Human Sensing Lab at Carnegie Mellon University. He earned his PhD in Deep Learning from the Universitat Politècnica de Catalunya. Albert previously worked at Epic Games, where he developed neural networks to facilitate and accelerate the design and animation of 3D characters. Before Epic Games, he served as a Computer Vision Engineer at Ficosa, where, he implemented and integrated computer vision algorithms for advanced driving assistance systems.
  • Nieto Salas, Juan José
    info

    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.
  • Peiró Lilja, Alex
    info

    Telecommunications Engineer from the Polytechnic University of Catalonia (UPC) and Master's in Intelligent Interactive Systems from Pompeu Fabra University (UPF). I research and develop speech synthesis and recognition for video games. Currently, I'm a Research Engineer at the Barcelona Supercomputing Center (BSC) on the AINA project, and a PhD student at the University of Barcelona (UB).
  • Pina Benages, Oscar
    info

    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
    info

    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
    / / /
    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.
  • Rafieian, Bardia
    info

    Doctoral student and researcher in the Computer Science department at the Universitat Politècnica de Catalunya (UPC). Holds a master’s degree in Software Engineering and Data Mining from Qazvin Azad University (QIAU). He currently works at Bechained.ai in MLOps, doing research and development on software integration, energy optimisation, recommender systems, NLP and time series forecasting. He has seven years of experience in data mining and natural language processing, and five years in machine learning and software integration.
  • Ruiz Hidalgo, Javier
    info
    / / /
    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
    info

    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.
  • Solé Gómez, Jaume Alexandre
    info
    / / /
    Pursuing a PhD at the Image Processing Group, UPC, under the supervision of Javier Ruiz-Hidalgo. Interested in topics related to graph neural networks and self-supervised learrning. Previously, Research Fellow at Istituto Italiano di Tecnologia, Research Assistant at Vicomtech, and Research Assistant at TU Delft. MSc in Telecommunications Engineering (2020) and a BSc in Telecommunications Technologies and Services Engineering (2018) at Universitat Politècnica de Catalunya. During my studies, I did stays at Télécom ParisTech, TU Delft, and University of Luxembourg.
  • Tarrés Benet, Laia
    info

    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
    info
    / / /
    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
    info
    / /
    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
  • Crisalix
  • Institut de Robòtica Industrial, CSIC-UPC
  • Weights & Biases

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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Programme: Artificial Intelligence with Deep Learning

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ENROLMENT CONDITIONS OF THE FUNDACIÓ POLITÈCNICA DE CATALUNYA

Introduction

The Fundació Politècnica de Catalunya (FPC), with tax identification number G60664000, registered in the Register of Foundations of the Government of Catalonia under no. 834, designs, promotes and manages the continuing education programmes of the Universitat Politècnica de Catalunya (UPC), as well as other training activities it promotes. The academic regulation of the UPC's continuing education programmes is set out in Agreement CG/2025/02/35, of 25 March, of the Consell de Govern, which approves the update of the amendment to the regulations governing continuing education programmes. And Articles 36 and 37 of Royal Decree 822/2021, of 28 September, which establishes the organisation of university education and the procedure for ensuring its quality. The UPC Social Council approves the price of continuing education programmes, as well as discounts, grants and ancillary services for each academic year.


Provision, knowledge and acceptance of the Enrolment Conditions

Reading and accepting these Enrolment Conditions is an essential requirement for studying at the FPC, as they constitute the basis of the training services contract that participants sign electronically with the FPC. These Enrolment Conditions are available to users on the Transparency Portal: . These Conditions are applicable to all courses taught by the FPC, with the exception of those aimed at other university institutions and public and private entities, which shall be governed by the legal instruments binding on the participating institutions.


Admission and registration fees

The admission process may require the advance payment of registration fees; this amount will be deducted from the tuition fee once admitted and will only be refunded in the event of non-admission, deferral or non-completion of the course, in the latter case, only if the letter of admission is valid. The admission process concludes with the sending of the admission letter to the participant, which includes the details of the course, the period of study, the tuition fees and the payment deadlines.


Enrolment

The enrolment process is formalised with the first payment of the course fee, whether partial or total. Therefore, this first payment of the course fee corresponds to the signing of the contract for the provision of training services regulated in these Conditions, regardless of whether the total price or any of the agreed payment instalments have been paid in full.

It is the participant's responsibility to find out about discounts on the enrolment fee and to prove that they meet the relevant criteria to benefit from them, always prior to formalising their enrolment; otherwise, they will not be able to benefit from them. Discounts and grants cannot be combined, unless they are not incompatible and this is expressly stated.

The participant declares that they are aware of and accept the calls for applications and the terms and conditions of the financial aid corresponding to the current academic year for which they have applied to be a beneficiary. If the student does not complete or withdraws from the course for which they have received aid, a discount or a grant within the period specified in the corresponding call for applications or in the enrolment document, they must return the amount granted or deducted from the price to the FPC. The provisions of the section on Changes to enrolment in these conditions shall not apply to this participant.

Enrolment is personal and non-transferable, so that once formalised, it shall only entitle the natural person who has been identified as a candidate and, subsequently, as an admitted person to undertake the course of study.

The enrolment fee may be paid in full or in part by third parties, although the right/obligation to follow the training course corresponds to the participant, without the paying party being able to interfere with or prevent the exercise of this right in any way. The foregoing is without prejudice to the FPC's right to prevent the participant from continuing in the cases provided for in the section on Non-payment of the enrolment fee provided for in these Conditions.

The amount paid for enrolment will not be refunded once the study has begun, nor after 14 calendar days from the date of payment. Otherwise, the right of withdrawal may be exercised. The amount paid will only be refunded if the study is postponed or does not take place.

Notwithstanding the above, and on an exceptional basis, the enrolment fee will be refunded if the following circumstances arise:

  • Visa refusal: this must be proven with the letter of refusal and always before the start of the study; and
  • Serious illness or accident of the participant: this must be proven by an official medical certificate, stating the initial date of the illness and the expected period of convalescence, always before the start of the study.

Only in these two cases will the FPC refund the total amount paid by the participant minus 300 euros for academic record processing costs.


Subsidised training

The FPC is not responsible for fulfilling the academic and/or administrative requirements for the contracted training to be subsidised. The participant or the person paying for the training does so at their own risk and exempts the FPC from any liability or compensation.


Change of enrolment

Requests to change enrolment, whether in terms of study or teaching method, must be made within 15 calendar days of the start date of the original course. Requests made after this period will not be accepted. The request will be assessed and its suitability determined on a case-by-case basis. When the change involves an increase in the total enrolment fee, the participant will be responsible for the difference. When the change involves a decrease in the enrolment fee, the difference will be refunded. Once evaluated, and regardless of the outcome of the request, changes to enrolment will incur a cost of €300 for the applicant for the processing of academic records, unless the change is due to causes attributable to the FPC.


Refund of tuition fees

The FPC reserves the right to cancel or postpone a study due to a lack of participants. Affected participants may choose between participating in another study or requesting a refund of the amount paid within one month of notification by the FPC. If no response is received, the amount paid will be used to support other students. The FPC will not provide any additional compensation and/or indemnification in the event of cancellation or postponement of a study programme or changes in its delivery. In the event that the FPC makes changes that do not substantially affect the content of the study programme, the place of delivery, the timetable and/or the start date, the participant will not be entitled to a refund of the registration fee or any additional compensation.


Non-payment of tuition fees

Failure to pay the total or partial amount of the tuition fees within the established deadlines may result in the suspension or termination of the training service under the terms indicated below. The FPC is authorised to take whatever action it deems appropriate to suspend the service; on the one hand, in the academic sphere, by suspending the academic record, denying access to teaching in the classroom (face-to-face or online), limiting access to the virtual campus, not assessing any of the subjects and making it impossible to continue with internship agreements, among other measures; and on the other hand, in the administrative and legal sphere, by undertaking the corresponding claims and actions for compensation.

Students who have outstanding debts with the FPC or who have not passed all the credits necessary to complete their studies before the end date of the course will not be able to obtain their degree or certificate, as applicable. Individuals with outstanding amounts payable to the FPC will not be able to enrol in any new studies offered by the FPC until the outstanding amount has been paid.

Finally, the FPC reserves the right to permanently suspend enrolment (automatic withdrawal), without any obligation to refund any amount, in the following cases:

  • Lack of accuracy and/or validity of the information and documentation provided and failure to respond to documentation requirements;
  • Non-payment of part or all of the enrolment fee within the agreed deadlines;
  • Engaging in any behaviour, expression or content that is defamatory, illegal, offensive or that undermines the values and dignity of individuals (teachers, participants, management staff, etc.) or the good image and reputation of the FPC, whether in physical or virtual environments, including social networks.

Right of withdrawal

Participants in a study may exercise their right of withdrawal for a period of 14 calendar days from the date of enrolment, provided that the study has not yet begun. Therefore, by reading and accepting these Terms and Conditions, participants are informed that once the course has begun or is in progress, the right of withdrawal no longer applies, in accordance with the provisions of Article 103 a) of Law 3/2014 of 27 March, which amends the revised text of the General Law for the Defence of Consumers and Users and other complementary laws, approved by Royal Legislative Decree 1/2007 of 16 November, and related regulations.


Teaching

The place and/or date of teaching may change for academic reasons (changes and adjustments to the calendar, need for additional teaching resources, etc.) and for organisational and logistical reasons (adaptation of spaces). Participants will be notified of any such changes at least 15 calendar days before the start of the course. Specific and temporary changes will be notified in advance.


Right to a degree/certificate

Upon completion of a continuing education master's degree, specialisation diploma or expertise diploma, students are entitled to receive a degree, issued by the rector of the University, for students with a previous university degree equivalent to level 2 of the Spanish Qualifications Framework for Higher Education (MECES), according to a standardised model. Students who do not provide proof of their university degree are entitled to obtain a certificate from the FPC, in accordance with a standard model. Short courses, with a workload of less than 15 ECTS credits, entitle students to obtain a certificate issued by the FPC, according to a standard model; however, if these courses include so-called micro-accreditations, students will obtain a digital accreditation issued by the University and recognised in the countries participating in Europass or equivalent. Degrees are issued in Catalan and English and, at the student's request, in Spanish and English. In the case of joint degrees, the issuance must comply with the provisions of the corresponding collaboration agreement.


Accreditation of university qualifications and other documents

If the required documentation is not not submitted before the last day of study for those degrees that require it, or if it is not authentic and/or sufficient, the degree will not be issued, even if the participant has passed the course.


Dispute resolution

The training services provided by the FPC are, in all cases, subject to private law. Any interpretation or divergence arising from these Terms and Conditions shall be the responsibility of the FPC. In the event of disagreement, the dispute shall be subject to private law and the courts of the city of Barcelona with ordinary civil jurisdiction, with express waiver of any other jurisdiction that may apply.


Belongings in the event of theft

Neither the FPC nor its staff shall be liable for any loss, damage or theft of any type of personal or similar items carried by participants or other occasional users of the facilities, who must pay special attention to their belongings at all times.


Organisation of teaching in exceptional circumstances

The FPC organises teaching in a flexible environment that allows it to adapt to any unforeseen circumstances that may arise, as well as to any regulations that may be established by the authorities. If at any time the authorities (university, health or any other competent body) recommend limiting face-to-face teaching as much as possible, the FPC, in coordination with these authorities, will take the necessary measures to implement this recommendation, and, as a result, teaching activities may become 100% online during the period established in the relevant recommendation, without the need to declare a state of emergency and/or suspend face-to-face teaching activities and/or implement formal lockdown or mobility restriction measures.


Barcelona, 15 October 2025


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