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Start   >  Master's & postgraduate courses  >  Education  >  Postgraduate course in Artificial Intelligence with Deep Learning
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10% discount if you enrol before 30th June

Program

Edition
2nd Edition
Credits
15 ECTS (120 teaching hours)
Modality
Face-to-face
Language of instruction
English
Fee
3.900 €
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: November 2019
End date: March 2020
Timetable
Tuesday: 18:30 to 21:30
Thursday: 18:30 to 21:30
Taught at
Tech Talent Center
C/ de Badajoz, 73-77
Barcelona
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 glassdoor.com 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.

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 (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 15h
Computer Vision
  • Image and video classification.
  • Object detection, tracking and segmentation.
  • Visual search.
  • 3D recognition and reconstruction.
  • Visual saliency prediction
2 ECTS 15h
Natural Language Processing
  • Word embeddings and language models.
  • Text processing, classification and summarization.
  • Neural Machine Translation (NMT).
  • Dialog systems.
  • Recommender systems.
2 ECTS 15h
Speech and Audio Processing
  • Speech recognition, conversino and synthesis.
  • Music processing.
  • Acoustic events.
  • Cross-modal processing: audio, language and vision.
2 ECTS 18h
Applications
  • Real case applications of deep learning in industry.
  • Research projects of leading scientific researchers.
3 ECTS 18h
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. Sincronization 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.

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.
Visits
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.
Tutorship
Students are given technical support in the preparation of the final project, according to their specialisation and the subject matter of the project.
Assesment 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 appear annually of the UPC School of Professional & Executive Development employment service .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 work and communication platform for students, lecturers and course directors and coordinators. My_Tech_Space allows students to find background material for their classes, to work in teams, ask their lecturers questions, consult their marks, etc.

Teaching team

Academic management
  • Giró Nieto, Xavier
    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
    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.
  • Drozdzal, Michal
    Michal Drozdzal is a research scientist at Facebook AI Research (FAIR). He received his Ph.D. from the University of Barcelona and master's degree from Wroclaw University of Technology. Before joining FAIR, Michal worked as post-doctoral researcher at Medtronic GI, Polytechnique of Montreal, and Montreal Institute for Learning Algorithms (MILA).
  • 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.
  • España i Bonet, Cristina
    PhD in Astrophysics and Cosmology from the UB and Master in Artificial Intelligence from the UB/UPC/URV. She currently works at the Universität des Saarlandes (UDS) and the Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI), in Germany. Her work encompasses natural language processing, information retrieval and machine learning. Her main interests include multilingual techniques based on classical methods as well as deep learning and applications for languages ​​with few resources.
  • Fojo Àlvarez, Daniel
    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
    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
    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 (www.cvc.uab.es). My present research interests are on development of learning-based approaches specially deep elarning applied to computer vision.
  • Leal Taixe, Laura
    Is an assistant professor at TUM, leading the Dynamic Vision and Learning group at the Technical University of Munich, Germany. She received her Bachelor and Master degrees in Telecommunications Engineering from the Technical University of Catalonia (UPC). She did her Master Thesis at Northeastern University, Boston and received her PhD degree (Dr.-Ing.) from the Leibniz University Hannover. She also spent two years as a postdoc at the ETH Zurich and one year at TUM. In 2017, she received the Sofja Kovalevskaja Award with 1.65 million euros.
  • Luque Serrano, Jordi
    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
    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).
  • Mcguinness, Kevin
    Assistant Professor with the School of Electronic Engineering in Dublin City University teaching Data Analytics and Machine Learning. Ph.D in Electronic Engineering (Computer Vision). Science Foundation Ireland Funded Investigator with the Insight Centre for Data Analytics. Research focuses on machine learning, deep learning, and applications in computer vision. 60+ peer reviewed publications including 12 Journal articles and 2 book chapters.
  • Mohedano Robles, Eva
    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
    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
    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
    Master's degree in Artificial Intelligence from UPC. Computer Scientist from UPC. Currently, is a PhD at the Signal Theory and Communications department of UPC working on neural machine translation.
  • Puch Giner, Santiago
    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.
  • Romero Soriano, Adriana
    Is a research scientist at Facebook AI Research and an adjunct professor at McGill University. Previously, she was a post-doctoral researcher at Montreal Institute for Learning algorithms, advised by Prof. Yoshua Bengio. Her postdoctoral research revolved around deep learning techniques to tackle the challenges posed by imaging multi-modality, high dimensional data and graph structures. Adriana received her Ph.D. from the University of Barcelona in 2015 with a thesis on assisting the training of deep neural networks with applications to computer vision.
  • Ruiz Costa-Jussà, Marta
    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.
  • Sayrol Clols, Elisa
    She holds an Engineering degree and a doctoral degree in Telecommunications Engineering from UPC. She was a visiting Scholar at Northeastern University and at the University of Southern California in the pass. She is currently Associate Professor at UPC and teaches undergraduate and graduate courses in Signals and Systems; Audiovisual Coding; Deep Learning and Biometrics. She has been Associate Dean and Dean of ETSETB. She has been Vicerector for Institutional Relationships at UPC. She currently collaborates with CARNET and is the Academic Director at UPC of the KIC on Urban Mobility.
  • 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
    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 https://torres.ai

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.



News

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