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Start   >  Master's & postgraduate courses  >  Education  >  Postgraduate course in Artificial Intelligence with Deep Learning
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1st Edition
15 ECTS (120 teaching hours)
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: 02/11/2020
End date: 19/04/2021
Monday: 6:30 pm to 8:30 pm
Wednesday: 6:30 pm to 8:30 pm
Taught at
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 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.


  • 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.

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, conversion 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. 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.

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. Both practical & lecture sessions with students will also be recorded and made available to registered students for their review.

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
  • 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.
  • Campos Camúñez, Víctor
    View profile in Linkedin
    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 German Research Center for Artificial Intelligence (DFKI), Columbia University, and Salesforce Research. His research interests focus on large scale machine learning.
  • 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

    The holder of a Master's degree in Telecommunications Engineering from ETSETB-UPC, specialising in multimedia (Deep Learning on vision, speech and text). She currently works as a Data Scientist at Telefónica Research, designing and developing machine learning and deep learning models for anomaly detection in networks. She is a member of Young IT Girls, a non-profit organisation to encourage girls into tech.
  • Escolano Peinado, Carlos

    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.
  • Favory, Xavier
    View profile in Linkedin
    Master degree in Acoustics, Signal processing, Informatics applied to Music (ATIAM), Institut de Recherche et Coordination Acoustique/Musique (IRCAM) and Master degree in Electronic Engineering from the École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA). Currently, pursuing a PhD in Informatics applied to Music Technologies at the Music Technology Group, Pompeu Fabra University (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
    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.
  • Luque Serrano, Jordi
    View profile in Linkedin
    Ph.D. by the Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (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.
  • 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.
  • 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.
  • 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.
  • Ruiz Costa-Jussà, Marta
    View profile in futur.upc / View profile in Linkedin
    Doctor of Telecommunications Engineering from the Universitat Politècnica de Catalunya (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 Computer Science Laboratory for Mechanics and Engineering Sciences (LIMSI) of the French National Center for Scientific Research 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.
  • Torres i Viñals, Jordi
    View profile in futur.upc / View profile in Linkedin
    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

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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