Deep Learning for Artificial Intelligence

Master Course at Universitat Politècnica de Catalunya (Autumn 2018)

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.


Lectures - Room: D5-010

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

  • 17/09 14:00 D01L1 (XG) Welcome
  • 17/09 15:00 D01L2 (XG) The Perceptron [Slides] [Video]
  • 01/10 14:00 D02L1 (ES) Multi-Layer Perceptron [Slides]
  • 01/10 15:00 D02L2 (ES) Backpropagation [Slides]
  • 08/10 14:00 D03L1 (JR) Loss functions [Slides]
  • 08/10 15:00 D03L2 (VV) Optimizers [Slides]
  • 15/10 14:00 D04L1 (VV) Convolutional Neural Networks [Slides]
  • 15/10 15:00 D04L2 (XG) Learning without Annotations [Slides] [GIF]
  • 22/10 14:00 D05L1 (RM) Transfer learning & Domain Adaptation [Slides] [Video]
  • 22/10 15:00 D05L2 (XG) Reinforcement learning: MDP, DQN [Slides] [Video]
  • 29/10 14:00 D06L1 (XG) Midterm exam
  • 29/10 15:00 D06L2 (RM) Incremental learning [Slides] [Video]
  • 05/11 14:00 D07L1 (SP) Generative models: VAE [Slides] [Video] [[GIF]][d09l1-gif]
  • 05/11 15:00 D07L2 (JR) Methodology
  • 12/11 14:00 D08L1 (MC) Recurrent Neural Networks I
  • 12/11 15:00 D08L2 (MC) Recurrent Neural Networks II
  • 19/11 14:00 D09L1 (SP) Generative models: GANs [Slides] [Video]
  • 19/11 15:00 D09L2 (SP) Generative Models: Likelihood models [Slides][GIF]
  • 03/12 14:00 D10L1 (MC) Attention-based models I
  • 03/12 15:00 D10L2 (MC) Attention-based models II
  • 10/12 14:00 D11L1 (XG) Reinforcement Learning (Reloaded) [Slides] [GIF]
  • 17/12 16:00 (Guests) Petia Radeva (UB-CVC) and Xavi Gonzalvo (Google AI)

Labs (15%) - Room: D5-010

The course will contain guided hands on lab that will lead the students in their first steps in deep learning frameworks.


Project (40%) - Rooms: D5-004

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. Powered by Google Cloud Education.


Practical details



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 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). Industry: Those industry members interested in the topic are encouraged to apply for the UPC School postgradute course on Artificial Intelligence with Deep Learning, running between February and July 2019.


Google Cloud


AWS Educate

GitHub Education




Universitat Politècnica de Catalunya