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Artificial Intelligence with Deep Learning

Postgraduate course. Face-to-face.

Presentation

UPC School

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.

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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.

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.




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.

Content

Subjects

Deep Learning
4 ECTS. 39 teaching hours.
  • 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.
Computer Vision
2 ECTS. 15 teaching hours.
  • Image and video classification.
  • Object detection, tracking and segmentation.
  • Visual search.
  • 3D recognition and reconstruction.
  • Visual saliency prediction
Natural Language Processing
2 ECTS. 15 teaching hours.
  • Word embeddings and language models.
  • Text processing, classification and summarization.
  • Neural Machine Translation (NMT).
  • Dialog systems.
  • Recommender systems.
Speech and Audio Processing
2 ECTS. 15 teaching hours.
  • Speech recognition, conversino and synthesis.
  • Music processing.
  • Acoustic events.
  • Cross-modal processing: audio, language and vision.
Applications
2 ECTS. 18 teaching hours.
  • Real case applications of deep learning in industry.
  • Research projects of leading scientific researchers.
Project
3 ECTS. 18 teaching hours.
  • 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.

Management & Faculty

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.
  • Campos Camúñez, Víctor
    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. His research interests focus on large scale machine learning.
  • 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.
  • 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..
  • 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).
  • Pascual de la Puente, Santiago
    Máster en Ingeniería de Telecomunicaciones por la ETSETB-UPC. Actualmente doctorando en el departamento de Teoría de la Señal y Comunicaciones. La investigación principal de la tesis es sobre deep learning y tecnologías de inteligencia artificial para procesamiento de voz y apilcacions voz a voz. Acumula una experiencia teórica y aplicada de más de 4 años en deep learning y modelos generativos profundos.
  • 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.
  • 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

General information

Credits
15 ECTS (120 teaching hours)
Start date
Start date:12/02/2019End date:04/07/2019
Timetable
Tuesday  18:30 to 21:30Thursday  18:30 to 21:30
Taught at
Tech Talent Center
C/ de Badajoz, 73-77
Barcelona
map
Contact
Telephone: (34) 93 114 68 05
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.

In the case of having a foreign degree check here.
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.
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.
Registration fee
3.800 €
The registration fee must be paid before the beginning of this Postgraduate course.
See the section Discounts, loans and financial aid for possibilities of advantageous financing conditions.

Applicants are given the option of making a voluntary €5 contribution when formalising their enrolment. As part of the UPC's 0.7% Campaign, this donation will go towards meeting charitable needs in developing countries.

0.7%

Language of instruction
English

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Collaborators