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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


2nd 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: 05/11/2019
End date: 09/04/2020
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 Universitat Politècnica de Catalunya. 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.
  • Escur i Gelabert, Janna
    Bachelor's degree in Engineering of Telecommunications from the UPC, with Audiovisual Systems speciality. Currently enrolled in the UPC Master in Advanced Telecommunication Technologies, Multimedia Processing track. Working with the Deep Learning team at Crisalix.
  • 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. Student of Master 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.
  • Hernando Pericas, Francisco Javier
    View profile in futur.upc / View profile in Linkedin
    Received the M.S. and Ph.D. degrees in telecommunication engineering from the UPC in 1988 and 1993, respectively. He is currently a Full Professor and the Director of the Research Center for Language and Speech. During the academic year 2002'2003, he was a Visiting Researcher in the Panasonic Speech Technology Laboratory, Santa Barbara, CA, USA. He has led the UPC team in several European, Spanish, and Catalan projects. Founder of Biometric Technologies, S.L. and Herta Security, S.L..
  • Ivanova Radeva, Petia
    Ph.D. degree from the Universitat Autònoma de Barcelona. Currently. I'm full professor at the University of Barcelona. I'm Head of Computer Vision and Machine Learning Research Group at the University of Barcelona and Head of Medical Imaging Laboratory (MILab) of Computer Vision Center ( My present research interests are on development of learning-based approaches specially deep elarning applied to computer vision.
  • Luque Serrano, Jordi
    View profile in Linkedin
    Is currently a research scientist in the scientific group at Telefónica I+D, in Barcelona, specialised in applied machine learning and algorithms from statistical physics to speech and language applications and the study of quantitative linguistics. He received his MSc and PhD from the UPC in 2005 and 2012, respectively. His research focuses on exploring and developing efficient algorithms for low-resource speech recognition, conversational agents and the prediction of human behaviour together with its application to business analytics.
  • 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 Universitat Politècnica de Catalunya. He received a BSc in Audiovisual Systems Engineering from Universitat Politècnica de Catalunya 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 Universitat Politècnica de Catalunya 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).
  • Puch Giner, Santiago
    View profile in Linkedin
    MSc in Computer Vision by Universitat Autònoma de Barcelona. Bachelor's degree in Telecommunications Engineering by Universitat Politècnica de Catalunya. Currently working at QMENTA Inc, a medical software company, as a Deep Learning Engineer. With more than 2 years of experience in the medical imaging space and several publications in highly ranked conferences.
  • Pumarola Peris, Albert
    I'm a third year PhD student at IRI under the supervision of Francesc Moreno-Noguer and Alberto Sanfeliu. My primary research interests are in the areas of Deep Learning and Computer Vision. In particular, my current research focuses on generative models.
  • Ramon Maldonado, Eduard
    View profile in Linkedin
    PhD candidate in Deep Learning for 3D Reconstruction at UPC. Master's degree in Computer Vision from UAB School. He is currently leading the AI team at Crisalix, responsible for developing novel algorithms for 3D Reconstruction using Deep Learning techniques.
  • 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
    Doctor in Information Technologies from UPF. He is currently a research scientist in the Telefónica I+D research group. 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.
  • Torres i Viñals, Jordi
    View profile in futur.upc
    Professor at UPC and research manager at BSC with 30 years of experience in teaching and research in supercomputing, with important scientific publications and R&D projects in companies and institutions. At the moment his research focuses on supercomputing applied to Artificial Intelligence. He is currently a Board Member of iThinkUPC & UPCnet, and acts as a trainer, mentor and expert for various organizations and companies; In turn, he has also written several technical books, gives lectures and has collaborated with different media, radio and television. More information at

Associates entities

Collaborating partners
  • Intelligent Data Science & Artificial Intelligence Research Center

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.

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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:

1. Complete and confirm your personal details.

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

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