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Start   >  Master's & postgraduate courses  >  Education  >  Postgraduate course in Artificial Intelligence with Deep Learning
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20-12-2021 + info

10-02-2022 + info

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  • discount
    This programme is part of the Employment Help grants programme


7th Edition
15 ECTS (120 teaching hours)
Blended learning
Face-to-face sessions. They will be held on Wednesdays. In these sessions, the theoretical foundations of neural networks, their applications and their implementation in production are exposed.

Online and live sessions. They will be held on Mondays. In these sessions, programming exercises will be completed.
Language of instruction
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
Start date: 14/02/2022
End date: 13/07/2022
Monday: 6:30 pm to 9:30 pm
Wednesday: 6:30 pm to 9:30 pm
Taught at
Tech Talent Center
C/ de Badajoz, 73-77
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 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 bring a laptop to the sessions of the program that will be specified in the academic calendar. The computer does not require any special hardware or software, only the Google Chrome browser.

Training Content

List of subjects
4 ECTS 40h
Blended learning
Deep Learning
  • Introduction to machine learning. Evaluation metrics.
  • The perceptron and the multi-layer perceptron.
  • Convolutional, recurrent and graph networks. Attention models.
  • Supervised, non-supervised and reinforcement learning.
  • Backpropagation, population-based and neuroevolution training.
  • Optimization. Batch normalization.
  • Generative models
  • Transfer learning. Incremental learning and catastrophic forgetting.
2 ECTS 14h
Blended learning
Computer Vision
  • Image and video classification.
  • Object detection, tracking and segmentation.
  • Visual search.
  • 3D recognition and reconstruction.
  • Visual saliency prediction
2 ECTS 14h
Blended learning
Natural Language Processing
  • Word embeddings and language models.
  • Text processing, classification and summarization.
  • Neural Machine Translation (NMT).
  • Dialog systems.
  • Recommender systems.
2 ECTS 14h
Blended learning
Speech and Audio Processing
  • Speech recognition, conversion and synthesis.
  • Music processing.
  • Acoustic events.
  • Cross-modal processing: audio, language and vision.
1 ECTS 9h
Blended learning
Reinforcement Learning
  • Markov Decision Processes.
  • Policy gradients.
  • Deep Q-Learning.
  • Actor-Critic.
1 ECTS 9h
Blended learning
  • Real case applications of deep learning in industry.
  • Research projects of leading scientific researchers.
3 ECTS 20h
Blended learning
  • 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.
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.

There will be two types of sessions:

  • Face-to-face (50%).
  • Live online (50%).

Face-to-face sessions cover theoretical concepts through master lectures and problem solving. Online-live sessions promote an active learning from the student, by integrating them into different teams organized in virtual rooms.

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
  • Giró Nieto, Xavier
    View profile in futur.upc
    Associate professor at the Universitat Politècnica de Catalunya (UPC) leading a research team on deep learning for multimedia. He has been a visiting scholar at Columbia University. He is currently working in partnership with the Barcelona Supercomputing Center in projects funded by Facebook, La Caixa, and the Catalan and Spanish public administrations. He has created a broad range of deep learning courses at the Barcelona School of Telecommunications Engineering (ETSETB) of UPC.
Teaching staff
  • Bou Balust, Elisenda
    View profile in futur.upc
    PhD in Telecom Engineering from Universitat Politècnica de Catalunya (UPC). MsC Aerospace Engineering from UPC-Massachusetts Institute of Technology (MIT). Currently, co-founder and CTO of Vilynx, where she is leading an engineering team of more than forty people devoted to build the first self-learning AI brain. Has more than ten years and experience in complex distributed systems, task scheduling and artificial intelligence, and is deeply interested in Knowledge Graphs/Ontologies, Self-Learning, Emergence and Reasoning.
  • 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.
  • 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.
  • Giró Nieto, Xavier
    View profile in futur.upc
    Associate professor at the Universitat Politècnica de Catalunya (UPC) leading a research team on deep learning for multimedia. He has been a visiting scholar at Columbia University. He is currently working in partnership with the Barcelona Supercomputing Center in projects funded by Facebook, La Caixa, and the Catalan and Spanish public administrations. He has created a broad range of deep learning courses at the Barcelona School of Telecommunications Engineering (ETSETB) of UPC.
  • Lapedriza i Garcia, Agata
    View profile in Linkedin
    She received her PhD in Computer Science from the Autonomous University of Barcelona (UAB) and holds a degree in Mathematics from the University of Barcelona (UB). She is currently a professor at the Universitat Oberta de Catalunya (UOC) and a visiting researcher at the Massachusetts Institute of Technology (MIT) Media Lab. She has been a visiting professor at the Massachusetts Institute of Technology (MIT), the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the MIT Media Lab and Google Cambridge. Her research interests are computer vision, natural language processing, affective artificial intelligence (AI), explainable AI, and AI for the social good.
  • 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.
  • Mosella Montoro, Albert
    View profile in Linkedin
    PhD Candidate at UPC. He received a BSc in Audiovisual Systems Engineering from Universitat Politècnica de Catalunya (UPC), after completing his thesis on object detection in collision path. In 2017 he received a MSc in Computer Vision from UAB-UPC-UPF-UOC, after completing his thesis on vehicle detection using instance segmentation as a result of a collaboration between UPC and Adasens Automotive GmbH. Currently, his main research topics are 3D Scene Understanding and 3D deep learning techniques.
  • Nieto Salas, Juan José
    View profile in Linkedin
    Holder of a bachelor’s degree in Telecommunications Engineering from the UPC. After finishing his Bachelor’s Thesis at the Insight Centre of Data Analytics in Dublin, he continued to work there on Reinforcement Learning as a research assistant. He is currently taking a Master’s Degree in Data Science at the UPC, and working as an intern at Telefonica Research.
  • 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.
  • Ruiz Costa-Jussà, Marta
    View profile in futur.upc / View profile in Linkedin
    The holder of a doctorate in Telecommunications Engineering from the Polytechnic University of Catalonia (UPC). She is a researcher at the UPC and has been awarded a Starting Grant from the European Research Council (ERC). She heads the Lifelong UNiversal lAnguage Representation - LUNAR - project. She has worked in the Mechanical and Engineering Sciences Computer Laboratory (LIMSI) of the Centre Nacional Francès d'Investigacions Científiques (CNRS) in Paris, at the Barcelona Media Innovation Centre, at the University of São Paulo, at the Infocomm Research Institute in Singapore, the National Polytechnic Institute of Mexico and the University of Edinburgh.
  • Suau Cuadros, Xavier
    View profile in Linkedin
    Xavier holds a PhD in Computer Vision and Machine Learning from BarcelonaTech. Before that, he graduated from the Universitat Politècnica de Catalunya (UPC) in Telecommunications Engineering and from Supaéro (Toulouse, France) in Aeronautics and Space Engineering. He is currently a research scientist at Apple's Machine Intelligence team, where he conducts research in ML interpretability, with applications to network compression, transfer learning and controllability in various domains. Before joining Apple, Xavier was a co-founder of the start-up Gestoos, an AI centric company tackling human-machine interaction.
  • 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.

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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To start the enrolment process for this programme you must complete and send the form that you will find at the bottom of these lines.

Next you will receive a welcome email detailing the three steps necessary to formalize the enrolment procedure:

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2. Validate your curriculum vitae and attach any additional required documentation, whenever this is necessary for admission.

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Once the fee has been paid and we have all your documentation, we will assess your candidacy and, if you are admitted on the course, we will send you a letter of acceptance. This document will provide you with all the necessary information to formalize the enrolment process for the programme.

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