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Start   >  Master's & postgraduate courses  >  Education  >  Postgraduate course in Artificial Intelligence with Deep Learning
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  • discount
    This programme is part of the Employment Help grants programme


3rd Edition
15 ECTS (120 teaching hours)
Language of instruction
Notes 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: 27/02/2020
End date: 14/07/2020
Notes to dates.
Tuesday: 6:30 pm to 9:30 pm
Thursday: 6:30 pm to 9:30 pm
Taught at
Tech Talent Center
C/ de Badajoz, 73-77
Why this programme?

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 task that was unaffordable until now. Several companies are already applying this data-driven programming paradigm in their daily activity, 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.

In April 2018, the European Commission estimated that the investment in artificial intelligence in the EU during 2017 had been between 4,000 and 5,000 million euros, a figure that is expected to increase to 20,000 million euros in 2020. In the United Kingdom, the AI ​​Sector Deal between the public administration and industry has been promoted to keep the country among the leading ones in the sector. In France, the public administration has announced that it will inject 1,500 million euros to develop AI. In the United States, the main boost comes from technology giants such as Google, Facebook, Amazon or Microsoft, which are expanding their development and research centers around the world. Meanwhile, China has designed a national plan that aims to make the country the world leader in artificial intelligence, with the expectations of generating a business volume of 150,000 million dollars in 2030. In this context, it is not surprising that has chosen ¿data scientist¿ as the best job in the United States, being the skills in deep learning the most demanded.

Postgraduate in Artificial Intelligence with Deep Learning aims to satisfy this demand of professionals with a teaching team with extensive experience in research (publications at NIPS, ICLR, CVPR) and teaching courses (since 2016). Graduates will master both the theoretical concepts and the Its implementation on platforms such as Tensorflow and PyTorch, in order to develop models based on deep neural networks. This degree will also include sessions where industry professionals will show how these technologies are being applied to innovate.

  • 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 specialised hardware (CPU and GPU).
  • Implement solution in deep learning frameworks.
  • Develop projects powered by artificial intelligence.
Who is it for?
  • Graduates in telecommunications, computer science, maths 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.

Training Content

List of subjects
4 ECTS 39h
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 18h
Computer Vision
  • Image and video classification.
  • Object detection, tracking and segmentation.
  • Visual search.
  • 3D recognition and reconstruction.
  • Visual saliency prediction
2 ECTS 18h
Natural Language Processing
  • Word embeddings and language models.
  • Text processing, classification and summarization.
  • Neural Machine Translation (NMT).
  • Dialog systems.
  • Recommender systems.
2 ECTS 18h
Speech and Audio Processing
  • Speech recognition, conversino and synthesis.
  • Music processing.
  • Acoustic events.
  • Cross-modal processing: audio, language and vision.
1 ECTS 9h
Reinforcement Learning
  • Markov Decision Processes.
  • Policy gradients.
  • Deep Q-Learning.
  • Actor-Critic.
4 ECTS 18h
  • 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. Sincronization 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.

Learning methodology

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

Attendees must bring their laptop in some sessions specified in the academic calendar. The laptop does not require any special hardware or software, only the Google Chrome 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.
These visits are to specialist centres, companies in the sector or outstanding and important locations in the sector, in order to obtain knowledge in situ of development, production and demonstration environments within the programme.
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 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 ETSETB of UPC.
  • Ruiz Costa-Jussà, Marta
    View profile in futur.upc
    Doctor of Telecommunications Engineering from UPC. Master's Degree in Language and Speech Technologies and the European Master of Research in Information and Communication Technologies, both by the UPC. He has worked at the LIMSI-CNRS in Paris, at the Media Innovation Center of Barcelona, at the University of São Paulo, at the Infocomm Research Institute of Singapore and at the National Polytechnic Institute of Mexico. He is currently a researcher at Ramón y Cajal from the UPC and leads the DeepVoice and ALLIES projects.
Teaching staff
  • Bellver Bueno, Míriam

    B.S. degree in Telecommunications Engineering in UPC. During the B.S. thesis she started to work in computer vision problems in the Image Processing Group of the university. She also obtained her Master in Telecommunications in the same faculty, and completed the Master Thesis in ETH Zürich. In 2016 she obtained a PhD grant from Obra Social ``la Caixa'' through La Caixa-Severo Ochoa International Doctoral Fellowship program, to do her PhD in the Barcelona Supercomputing Center about computer vision using deep learning. Her main research topics are object detection and image segmentation.
  • Bou Balust, Elisenda
    View profile in futur.upc
    PhD in Telecom Engineering from UPC. MsC Aerospace Engineering from UPC-MIT. Currently co-founder and CTO of Vilynx, where she is leading an engineering team of more than 40 people devoted to build the first self-learning AI brain. Has more than 10y experience in complex distributed systems, task scheduling and AI, and is deeply interested in Knowledge Graphs/Ontologies, Self-Learning, Emergence and Reasoning.
  • Cámbara Ruiz, Guillermo

    Assistant Researcher at Telefónica I+D, specializing in deep learning applied to speech processing through biometrics and language applications. Formerly an R&D Engineer in G+D Mobile Security, worked as a lead tester specialized in eSIM operating systems. Received a BSc in Physics from Universitat of Barcelona in 2015, and currently finishing a MSc in Intelligent Interactive Systems at Universitat Pompeu Fabra.
  • Campos Camúñez, Víctor

    Holds a BsC and a MsC degrees on Electrical Engineering from UPC. He is currently pursuing his PhD on the intersection between Deep Learning and High Performance Computing at the Barcelona Supercomputing Center, supported by Obra Social "la Caixa" through La Caixa-Severo Ochoa International Doctoral Fellowship program. He has interned at the Deep Learning Competence Center at DFKI (2016), Columbia University (2017), and Salesforce Research (2019). His research interests focus on large scale machine learning.
  • Casas, Noe

    PhD candidate in Neural Machine Translation at UPC, conducting industrial research at Lucy Software. MSc in Artificial Intelligence from UNED. 2+ years of professional experience as data scientist. 10+ years of professional experience as software engineer and software architect at the aerospace industry.
  • Escolano Peinado, Carlos

    Master's degree in Artificial Intelligence from UPC. Computer Scientist from UPC FIB, Currently, is a PhD at the Signal Theory and Communications department of UPC working on neural machine translation.
  • Favory, Xavier
    View profile in Linkedin
    Holding two Master's degrees from France. One in Acoustics, Signal processing, Informatics applied to Music (ATIAM), IRCAM, Paris and another in Electronic Engineering from ENSEA, Cergy. Currently pursuing a PhD in Informatics applied to Music Technologies at the Music Technology Group, UPF. Academic experience in audio signal processing, machine learning and human-computer interaction, and practical experience in web application development.
  • Fojo Àlvarez, Daniel
    View profile in Linkedin
    R&D engineer at Disney Research. CFIS graduate of BSc in Mathematics and BSc in Engineering Physics and Master¿s Degree in Advanced Mathematics and Mathematical Engineering.
  • Giró Nieto, Xavier
    View profile in futur.upc
    Associate professor at the 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 ETSETB of UPC.
  • Luque Serrano, Jordi
    View profile in Linkedin
    Ph.D. by the Department of Signal Theory and Communications, UPC. Currently, is associate professor of the Department of Computer Science (CS) from UPC and research scientist into the scientific group at Telefónica I+D Innovation Lab. His research interests include the study of quantitative linguistics, low-resource speech recognition and deep learning signal processing. His industrial experience includes prototyping and benchmarking of novel algorithms for speech and language processing, their integration and deployment together with consulting, ideation and prospect of pioneering applications.
  • Masuda Mora, Issey
    View profile in Linkedin
    Currently Team Lead in the deep learning group at Vilynx, an AI company based in San Francisco & Barcelona. Broad experience developing deep learning models for a scalable and production-ready environments. Extensive use of TensorFlow as main DL framework. Solid background as a programmer (from software design to implementation) backed with experience working as a software engineering (formerly Trovit employee). Bachelor thesis in deep learning applied for Visual Question Answering finished with honours. Bachelor degree in Telecommunications Engineering CITEL (Top 5% class).
  • Mohedano Robles, Eva
    View profile in Linkedin
    PhD in Computer Vision from the Insight Centre for Data Analytics in Dublin City University (DCU), where developed a thesis in Content Based Image Retrieval "Deep Image Representations for Instance Search", supervised by Noel E. O'Connor and Kevin McGuinness. Graduated in Audiovisual Systems Engineering at UPC. Currently working as a post-doctoral researcher at the Insight Centre for Data Analytics working in multi-modal video analysis using deep learning.
  • Mosella Montoro, Albert
    View profile in Linkedin
    PhD Candidate at UPC. He received a BSc in Audiovisual Systems Engineering from UPC in 2015, 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.
  • Pascual de la Puente, Santiago
    View profile in futur.upc
    Master's degree in Telecommunications Engineering from the ETSETB-UPC. Currently PhD student in the Department of Signal Theory and Communications. The main research of the thesis is about deep learning and artificial intelligence technologies for voice processing and voice-to-speech applications. Accumulate a theoretical and applied experience of more than 4 years in deep learning and deep generative models.
  • Pons Puig, Jordi
    View profile in Linkedin
    Is finishing his PhD in music technology, large-scale audio collections, and deep learning at the Music Technology Group (Universitat Pompeu Fabra, Barcelona). Previously, he recieved a MSc in sound and music computing (Universitat Pompeu Fabra, Barcelona), and his BS was in telecommunications engineering (Universitat Politècnica de Catalunya, Barcelona). He also interned at IRCAM (Paris), at the German Hearing Center (Hannover), at Pandora Radio (USA, Bay Area), and at Telefónica Research (Barcelona).
  • Pumarola Peris, Albert

    Third year PhD student at IRI under the supervision of Francesc Moreno-Noguer and Alberto Sanfeliu. His primary research interests are in the areas of Deep Learning and Computer Vision. In particular, my current research focuses on generative models.
  • Ruiz Costa-Jussà, Marta
    View profile in futur.upc
    Doctor of Telecommunications Engineering from UPC. Master's Degree in Language and Speech Technologies and the European Master of Research in Information and Communication Technologies, both by the UPC. He has worked at the LIMSI-CNRS in Paris, at the Media Innovation Center of Barcelona, at the University of São Paulo, at the Infocomm Research Institute of Singapore and at the National Polytechnic Institute of Mexico. He is currently a researcher at Ramón y Cajal from the UPC and leads the DeepVoice and ALLIES projects.
  • Segura Perales, Carlos

    Associate Researcher at Telefonica Research in Barcelona, Spain. From 2011 to 2015 he worked at the company Herta Security as the Director of Innovation under the Torres Quevedo program, where his main duties were researching and developing algorithms for speaker and face recognition. He has participated in three national research projects and three EU research projects, and has published many scientific papers in peer-reviewed international journals and international conferences. His research interests include deep learning, machine learning. speech processing, computer vision, and more lately, natural language processing and dialog systems.
  • Serrà Julià, Joan
    View profile in Linkedin
    Doctor in Information Technologies from UPF. He is currently a research scientist in the AI group at Dolby Laboratories. He is an expert in machine learning, deep learning, data mining, audio processing, recommendation systems and metaheuristics. He is co-author of more than 100 scientific publications in various fields, some of them of notable impact, and has participated in several european research projects. He conducts seminars, teaching and divulgation talks, lately always related to deep learning.

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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