2nd Summer School
on Deep Learning for Computer Vision
Barcelona
Seminar at UPC ETSETB TelecomBCN (June 21-27, 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 and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Instructors
Instructors
Teaching Assistants
Keynotes
Open Lectures by Guest Speakers
(registration required)
This 2017 edition of the seminar will include two invited talks on Tuesday 27 at 5pm in Aula de Teleensenyament, in the 2nd floor of the ETSETB TelecomBCN building B3, in Campus Nord UPC (directions). This session is open to the general public, but attendees must RSVP / register here.
Agata Lapedriza is an Associate Professor at the Universitat Oberta de Catalunya. She received her MS deegree in Mathematics at the Universitat de Barcelona in 2003, and her Ph.D. degree in Computer Science at the Computer Vision Center in 2009, at the Universitat Autonoma Barcelona. She was working as a visiting researcher in the Computer Science and Artificial Lab department at the Massachuetts Institute of Technology from 2012 until 2015. Her research interests are related to image understanding, scene and object recognition and affective computing.
Dr. Bou is the co-Founder and CTO of Vilynx, a startup devoted to change the way people consume online videos through machine Learning. Dr Bou is also an associate professor at the Universitat Politècnica de Catalunya (UPC), lead SW Architect at UPC Nano-Satellite Lab (NanoSatLab), and collaborates with the MIT Aero-Astro Dept and the UMD Aerospace Engineering Dept. She was a recipient of the 2013 Google Faculty Research Awards.
Photos
A collection of photos from the course is available in this album.
Practical
Practical
- Course codes: 230360 (Phd & master) / 230324 (Bachelor)
- ECTS credits: 2.5 (Phd & master) / 2 (bachelor) (corresponds to full-time dedication during the week course)
- Teaching language: English
- The course is offered for both master and bachelor students, but under two study programmes adapted to each profile.
- Class Dates: 21-27 June, 2017
- Class Schedule: 3-7pm (you will need 6 extra hours a day for homework during the week course)
- Capacity: 20 MSc students + 10 BSc students
- Location: Campus Nord UPC, Module D5, Room 010
Registration
Registration
Registration procedure depends on the student profile:
- Master and Bachelor students at ETSETB Telecom: Follow these instructions provided by ETSETB academic office. An extra period of registration is expected to open during June 2017. An extra period of registration will be available between May 29 and June 2.
- 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 ETSETB academic office before the extraordinary registration period and express your interest in the course. Once the registration procedure for ETSETB is completed, available seats will be allocated to external UPC applicants prioritzied on their profile.
- 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).
Contact
Piazza will be used for class discussion and communication, instead of the regular UPC Atenea platform. Piazza is highly catered to getting you help fast and efficiently from classmates, TAs and instructors. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com.
Find us at the class page.
Related
Previous editions
- Deep Learning for Computer Vision. UPC TelecomBCN 2016.
- Deep Learning for Speech and Language. UPC TelecomBCN 2017.
- Deep Learning for Multimedia. Inisght Dublin City University 2017.
- Xavier Giro-i-Nieto, “Deep learning for computer vision: Image, Object, Videos Analytics and Beyond”. LaSalle URL. May 2016.
Related courses
- Fei-Fei Li, Andrej Karpathy, Justin Johnson, “CS231n: Convolutional Neural Networks for Visual Recognition”. Stanford University, Spring 2016.
- Sanja Fidler, “Deep Learning in Computer Vision”. University of Toronto, Winter 2016.
- Hugo Larochelle, “Neural Networks”. Université de Sheerbroke.
- Joan Bruna, “Stats212b: Topics on Deep Learning”. Berkeley University. Spring 2016.
- Yann LeCun, “Deep Learning: Nine Lectures at Collège de France”. Collège de France, Spring 2016. [Facebook page]
- Dhruv Batra, “ECE 6504: Deep learning for perception”. Virginia Tech, Fall 2015.
- Vincent Vanhoucke, Arpan Chakraborty, “Deep Learning”. Google 2016.
- German Ros, Joost van de Weijer, Marc Masana, Yaxing Wang, “Hands-on Deep Learning with Matconvnet”. Computer Vision Center (CVC) 2015.
Sponsors
title: “Organizers” bg: #9AD1F5 color: black style: center fa-icon: cog —
ETSETB Telecom BCN
Universitat Politècnica de Catalunya
Insight Centre for Data Analytics
Dublin City University