Introduction to Deep Learning

Winter School at Universitat Politècnica de Catalunya (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 (30%) - Room: D5-010

Course will be divided in modules of half an hour covering the following topics:

  • 22/01 10:00 D1L1 (XG) Welcome
  • 22/01 10:30 D1L2 (VV) Machine Learning [PDF]
  • 22/01 11:00 D1L3 (AB) Perceptron [PDF]
  • 22/01 11:30 D1L4 (ES) Multi-Layer Perceptron [PDF]
  • 23/01 10:00 D2L1 (ES) Backpropagation [PDF]
  • 23/01 10:30 D2L2 (VV) Optimizers [PDF]
  • 23/01 11:00 D2L3 (JR) Loss functions [PDF]
  • 23/01 11:30 D2L4 (JR) Methodology [PDF]
  • 24/01 10:00 D3L1 (VV) Convolutional Neural Networks - CNNs [PDF]
  • 24/01 10:30 D3L2 (RM) Transfer learning [PDF]
  • 24/01 11:00 D3L3 (MRC) Recurrent Neural Networks - RNNs [PDF]
  • 24/01 11:30 D3L4 (MRC) Gated RNNs [PDF]
  • 24/01 10:00 D4L1 (MRC) Attention Models [PDF] [Video]
  • 25/01 10:30 D4L2 (XG) Beyond supervised learning [PDF]
  • 25/01 11:00 D4L3 (VV) Adversarial Training [PDF]
  • 25/01 11:30 D4L4 (XG) Architectures [PDF]
  • 30/01 11:30 Guest: Joost van de Weijer (Computer Vision Center) [Event] [PDF]
  • 30/01 12:15 Guest: Joan Serrà (Telefónica)- [Event] [PDF]

Labs (30%) - Room: D5-004

The course will contain guided hands on lab provided by the NVIDIA Deep Learning Institute.

Project (40%) - Room: D5-004

Students will work in teams to develop a machine learning research project that will be presented in an oral presentation during the final day of the course.

  • 22/01: Getting started
  • 23/01: First steps with Keras and TensorBoard
  • 24/01: Multi-layer Perceptron
  • 25/01: Convolutional Neural Networks
  • 30/01: Oral presentations

Practical details



Registration procedure depends on the student profile:

  • Bachelor students at ETSETB who can register the 2 ECTS: Follow the regular schedule from your academic office. There is an extraordinary registration period between 11 and 14 January 2018.
  • Bachelor students at ETSTEB who cannot register the 2 ECTS: Fill in this form.
  • Master students at ETSETB: This Winter School is a light version of the full MSc course of Deep learning for Artificial Intelligence, taught during the Autumn semester. So we encourage you to sign up for this other one. However, if you would to register to the Winter School on Deep Learning for Speech and Language (DLSL) or the Summer School on Deep Learning for Computer Vision (DLCV), you can still follow this introductory course in the morning and sign up officially for DLSL in the afternoons and for DLCV in July 2018. If this is your case, fill in this form so we can have a seat for you to the introductory one, and register to DLSL.
  • 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.
  • 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.
  • Industry members and any other profile: 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.

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Universitat Politècnica de Catalunya