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Start   >  Master's & postgraduate courses  >  Education  >  Postgraduate course in Artificial Intelligence with Deep Learning
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  • discount
    10% discount if you enrol before 16th September

Presentation

Edition
8th Edition
Credits
15 ECTS (120 teaching hours)
Delivery
Face-to-face
Language of instruction
English
Fee
€3,900 €3,510(10% discount if you enrol before 16th September)
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: 08/02/2023
Classes end: 12/07/2023
Programme ends : 19/07/2023
Timetable
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
Barcelona
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 glassdoor.com 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)
Aims
  • 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 a laptop with the Google Chrome browser. No special hardware or software is required for the computer.

Training Content

List of subjects
4 ECTS 32h
Deep Learning
  • Introduction to machine learning. Evaluation metrics.
  • The perceptron and the multi-layer perceptron.
  • Convolutional networks.
  • Backpropagation training.
  • Optimization. Batch normalization.
  • Interpretability.
  • Recommendation systems.
1 ECTS 9h
Sequence modeling
  • Recurrent Neural Networks (RNN).
  • Gating and attention mechanisms.
  • Transformer.
1 ECTS 9h
Unsupervised Learning
  • Self-supervised learning
  • Autoregressive Models
  • Variational Autoencoders (VAE).
  • Generative Adversarial Networks (GAN).
2 ECTS 15h
Computer Vision
  • Image and video classification.
  • Object detection, tracking and segmentation.
  • Visual search.
  • 3D recognition and reconstruction.
  • Transfer of learning.
2 ECTS 12h
Natural Language Processing
  • Word embeddings and language models.
  • Text processing.
  • Classification and summaries of texts.
  • Automatic translation.
  • Dialogue systems.
1 ECTS 9h
Speech and Audio Processing
  • Speech recognition, synthesis and improvement.
  • Music processing.
  • Transfer of learning.
1 ECTS 9h
Reinforcement Learning
  • Markov Decision Processes.
  • Policy gradients.
  • Deep Q-Learning.
  • Actor-Critic.
3 ECTS 25h
Project
  • 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.
Degree
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.



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.
Tutorship
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
Attendance
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
  • Gállego Olsina, Gerard Ion
    info
    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.
  • Pardàs Feliu, Montserrat
    info
    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.
  • Tarrés Benet, Laia
    info

    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.
Teaching staff
  • Bach Ramírez, Josep Maria
    info
    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
    info
    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.
  • Carós Roca, Mariona
    info
    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
    info
    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
    info
    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
    info
    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.
  • Giró Nieto, Xavier
    info
    View profile in futur.upc / View profile in Linkedin
    An applied scientist at Amazon Science Barcelona, in the field of deep learning applied to computer vision. He was the founder and director of the postgraduate course in Artificial Intelligence with Deep Learning for the first nine courses between 2019-2022, which he combined with his research and teaching at the Universitat Politècnica de Catalunya (UPC) and the Institute of Robotics and Industrial Informatics (IRI). He is a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and one of the instigators of the Deep Learning Barcelona Symposium (DLBCN).
  • Gómez Duran, Paula
    info
    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.
  • Nieto Salas, Juan José
    info
    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
    info
    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
    info
    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.
  • Tarrés Benet, Laia
    info

    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
    info
    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.



Testimonials

Testimonials

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
Testimonials<
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
Testimonials<

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