Deep Learning for Artificial Intelligence

Master Course at Universitat Politècnica de Catalunya (Autumn 2017)

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.

Instructors

Instructors

Xavier Giró
Marta R. Costa-jussà
Jordi Torres
Elisa Sayrol


Santiago Pascual
Veronica Vilaplana
Ramon Morros
Javier Ruiz




Teaching Assistants

Adrià Casamitjana
Noé Casas
Amanda Duarte


Guest Speakers

Oriol Vinyals
Google Deepmind
(London, UK)
Yannis Kalantidis
Facebook AML
(Menlo Park, USA)
Amaia Salvador
UPC Barcelona
(Catalonia / Spain)
Cristian Canton
Facebook AML
(Seattle, USA)


Lectures

Lectures - Room: D5-010

Course will be divided in modules of one hour covering the following topics (draft):

Labs (15%) - Rooms: D5-004, D5-005 & D5-007

The course will contain guided hands on lab that will lead the students in their first steps in deep learning frameworks. A summary can be found on this site by Prof. Jordi Torres.:

Project (40%) - Rooms: D5-004, D5-005 & D5-007

Students will work in teams to develop a machine learning research project that will be presented both in an oral presentation and as a poster during the final session open to the general public.

  • 26/09: Guidelines for Project (XG)
  • 17/10: Project Proposals (XG)
  • 07/11: Project Development (XG, AC, NC)
  • 14/11: Project Critical Review (XG, NC, AC)
  • 21/11: Project Development (XG, NC, AC)
  • 12/12: Project oral presentations (XG, NC, ES, AC)
  • 19/12: Poster presentations (open session) - RSVP here

Project pages with source code, slides and contact information for recruiters:

Practical

Practical details

Registration

Registration

Registration procedure depends on the student profile:

  • Master students at ETSETB and FIB: Follow the regular schedule from your academic office.
  • Mobility students: If your host institution has signed an agreement with UPC ETSETB Telecom BCN school, you can request a mobility from your host institution and sign up for the course under the same conditions as ETSETB students.
  • Students at UPC but not in ETSETB: Contact the your academic office and request being allowed to take this course. If accepted, contact ETSETB academic office and request more details.
  • Non UPC nor mobility students: You must apply for being accepted in the course and cover the 100% cost of the ECTS credits, without the support of the public funds. This corresponds to 143,08 € per ECTS credit (Summer 2016). If you are interested in this option, please contact the ETSETB Telecom BCN academic office, with an e-mail to secretaria@etsetb.upc.edu or calling at 93 405 4174 / 93 401 6772 / 93 401 5966 or 93 401 6750 in the morning (Monday to Thursday from 11 to 14 and Fridays from 11 to 13) or noons (Wednesdays and Thursdays from 16 to 17h).
Sponsors

Vilynx

Google Cloud

BSC

AWS Educate

GitHub Education

Nvidia

Organizers

ETSETB
ETSETB Telecom BCN

UPC
Universitat Politècnica de Catalunya

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