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Start   >  Master's & postgraduate courses  >  Education  >  Postgraduate course in Artificial Intelligence with Deep Learning
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  • discount
    New grants for this postgraduate course aimed at women
  • discount
    15% discount if you enrol before 30th June


3rd Edition
15 ECTS (120 teaching hours)
Language of instruction
€3,900 €3,315(15% discount if you enrol before 30th June)
Payment of enrolment fee options

The enrolment fee can be paid:
- In a single payment to be paid within the deadline specified in the letter of admission to the programme.
- In two instalments:

  • 60% of the amount payable, to be paid within the deadline specified in the letter of admission to the programme.
  • Remaining 40% to be paid up to 60 days at the latest after the starting date of the programme.
Notes 0,7% campaign

Registration open until the beginning of the course or until end of vacancies.
Start date
Classes start: 03/10/2022
Classes end: 08/03/2023
Programme ends : 22/03/2023
Monday: 6:00 pm to 8:00 pm
Wednesday: 6:00 pm to 8:00 pm
Taught at
Presentation video
Why this postgraduate course?

Artificial intelligence (AI) is at the core of the industrial revolution 4.0, based on the automatic processing of data. The availability of large volumes of data and computational resources with affordable costs has made possible 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. However, the same challenge repeats everywhere: the scarcity of professionals capable of understanding the potential and opportunities of these tools, as well as their implementation in a practical and scalable fashion.

According to the AI Index from Stanford University, in 2019, global private AI investment was over $70B, with startup investments over $37B after a steady average annual growth rate of over 48% since 2010. This has resulted in a significant increase of job postings which, in the US, grew from 0.3% in 2012 to 0.8% of total jobs posted in 2019. In Spain, 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. In terms of public funding, the EU funding for research and innovation for AI has risen to €1.5 billion between 2017 and 2019, i.e. a 70% increase compared to the previous period. This context explains why the job analysis portal has chosen data scientist as the best job 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 of.

Promoted by:
  • Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona. ETSETB (UPC)
  • Design deep learning models, especially for processing text, 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 40h
Deep Learning
  • Introduction to machine learning. Evaluation metrics.
  • The perceptron and the multi-layer perceptron.
  • Convolutional and graph networks.
  • Backpropagation.
  • Optimization. Batch normalization.
  • Transfer learning. Incremental learning and catastrophic forgetting.
2 ECTS 14h
Computer Vision
  • Image and video classification.
  • Object detection, tracking and segmentation.
  • Visual search.
  • 3D recognition and reconstruction.
  • Visual saliency prediction
2 ECTS 14h
Natural Language Processing
  • Word embeddings and language models.
  • Text processing, classification and summarization.
  • Neural Machine Translation (NMT).
  • Dialog systems.
  • Recommender systems.
2 ECTS 14h
Speech and Audio Processing
  • Speech recognition, conversion and synthesis.
  • Music processing.
  • Acoustic events.
1 ECTS 9h
Reinforcement Learning
  • Markov Decision Processes.
  • Policy gradients.
  • Deep Q-Learning.
  • Actor-Critic.
1 ECTS 9h
  • Real case applications of deep learning in industry.
  • Research projects of leading scientific researchers.
3 ECTS 20h
  • Programming in Python for deep learning.
  • Deep learning frameworks: Keras/TensorFlow and PyTorch/Caffe2.
  • Monitoring of neural network training: training curves, computational resources.
  • Data loaders. Synchronization between CPU and GPU.
  • Cloud computing.
The UPC School reserves the right to modify the contents of the programme, which may vary in order to better accommodate the course objectives.
Postgraduate diplomas issued by the Universitat Politècnica de Catalunya. Issued pursuant to art. 34.1 of Organic Law 4/2007 of 12 April, amending Organic Law 6/2001 of 21 December, concerning Universities. To obtain this degree it is necessary to have an official. Otherwise, the Fundació Politècnica de Catalunya will only award them a a certificate of completion. (Ver datos que constan en el certificado).

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.
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
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.

Teaching team

Academic management
  • Fojo Àlvarez, Daniel
    View profile in Linkedin
    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. A Data scientist at Glovo.
  • Pardàs Feliu, Montserrat
    View profile in futur.upc
    The holder of a doctorate in Telecommunications Engineering from the Universitat Politècnica de Catalunya (UPC). Professor in the Department of Signal Theory and Communications at the UPC, and a member of the Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC). She has led research and technology transfer projects in the field of image and video processing and computer vision - areas in which she publishes internationally. She has been a visiting researcher at Lucent Technologies (Bell Labs) and Toshiba's Cambridge Computer Vision Research Lab.
Teaching staff
  • Bach Ramírez, Josep Maria
    View profile in Linkedin
    Head of Data & AI at Codegram Technologies, which he co-founded. With over twelve years in the industry as a self-taught software engineer, he's currently focusing on the intersection between AI and industry, with a special interest in Deep Reinforcement Learning and Natural Language Processing.
  • Cámbara Ruiz, Guillermo
    View profile in Linkedin
    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
    View profile in Linkedin
    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
    View profile in Linkedin
    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.
  • Caselles Rico, Pol
    View profile in Linkedin
    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
    View profile in Linkedin
    Master's degree in Artificial Intelligence from the Universitat Politècnica de Catalunya (UPC). Computer Scientist from UPC FIB, Currently, is a PhD at the Signal Theory and Communications department of UPC working on neural machine translation.
  • Fojo Àlvarez, Daniel
    View profile in Linkedin
    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. A Data scientist at Glovo.
  • Gállego Olsina, Gerard Ion
    View profile in futur.upc / View profile in Linkedin
    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.
  • Gómez Duran, Paula
    View profile in Linkedin
    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.
  • Mcguinness, Kevin
    View profile in Linkedin
    Holds a doctorate in Electronic Engineering (Computer Vision). Assistant Professor at the School of Electronic Engineering at Dublin City University, teaching Data Analytics and Machine Learning. A Science Foundation Ireland-funded investigator with the Insight Center for Data Analytics. His research focuses on machine learning, deep learning, and applications in computer vision. He has authored more than seventy peer reviewed publications, including fourteen journal articles and 2 book chapters.
  • Nieto Salas, Juan José
    View profile in Linkedin
    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.
  • Pons Puig, Jordi
    View profile in Linkedin
    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.
  • Rafieian, Bardia
    View profile in Linkedin
    PhD student and researcher in Signal Theory and Communications Department at the Universitat Politècnica de Catalunya (UPC). Master in Software Engineering and Data Mining from Qazvin Azad University (QIAU). Currently, he works at Viume as a machine learning engineer doing research and development on software integration, natural language processing, recommender systems and computer vision. He has five years of experience in data mining and natural language processing and three years in machine learning, and software integration.
  • Sanchez Cervera, Ariadna
    View profile in Linkedin
    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. She is currently a Research Scientist in Amazon's Text-to-Speech team.
  • Tarrés Benet, Laia

    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.
  • Ventura Royo, Carles
    View profile in Linkedin
    Holder of a doctorate in Computer Vision from the UPC. He is currently a lecturer in Computer Science, Multimedia and Telecommunications at the Universitat Oberta de Catalunya (UOC), where he teaches courses about artificial intelligence, machine learning and computer vision. His research is focused on computer vision: object segmentation in images and videos and emotion recognition in videos. He is a member of the Scene Understanding and Artificial Intelligence (SUNAI) research group at the UOC.
  • Giró Nieto, Xavier
    View profile in futur.upc / View profile in Linkedin
    Senior lecturer at the Universitat Politècnica de Catalunya (UPC), specialising in deep learning applied to multimedia data. He has worked as a visiting researcher at Columbia University. He is currently a member of the Institute of Robotics and Industrial Informatics (CSIC-UPC) and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). He has been involved in the Deep Learning Barcelona Symposium and an extensive range of artificial intelligence courses at the Barcelona School of Telecommunications Engineering and the School of Industrial, Aerospace and Audiovisual Engineering of Terrassa.
  • Triginer Garcés, Gil
    View profile in Linkedin
    DPhil in Atomic and Laser Physics at the University of Oxford. Graduated in Enginyeria de les Telecomunicacions at Universitat Politècnica de Catalunya (UPC). In 2019 I joined Crisalix Labs as a deep learning (DL) researcher, focusing on applying DL techniques to the problem of 3D reconstruction.

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.



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 Postgraduate Course in
Artificial Intelligence with Deep Learning
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

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