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Short Course

Advanced Deep Learning for Computer Vision

Programme ScheduleDuration (days)Mode of DeliveryProgramme Code
To be advised2Synchronous e-LearningTGS-2023022211

Course Overview

Computer Vision is omnipresent in our digital society and its aim is to provide vision to computers for a wide range of applications. Recent developments in deep learning/artificial neural network techniques have greatly advanced the performance of visual processing systems. The objective of this course is to learn and implement neural networks for computer vision tasks. The module will cover learning algorithms, neural network architectures, and practicals for training networks for various visual recognition tasks.

Course Objectives

At the completion of the course, the participants will be able to:

  • Articulate advanced deep learning models, their strengths and constraints for Computer Vision.
  • Have basic coding skills to implement deep learning algorithms for several vision tasks.
  • Understand the most up-to-date deep learning architectures for CV.

Prerequisites

Basic Python programming skills

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Types of Available Fundings

  • SkillsFuture Credit
  • UTAP
  • PSEA
  • Enhanced Training Support
  • SFEC

    • Instructors


    A/Prof Xavier Bresson

    A/Prof Xavier Bresson

    A/Prof Xavier Bresson is an international leader in the field of deep learning. Particularly, he co-pioneered a new machine learning technology called graph neural networks (GNN), which combines graph theory and neural network techniques to tackle complex data domains. He has organised several international conferences and tutorials on graph deep learning such as the recent UCLA’21 workshop on “Deep Learning and Combinatorial Optimization”, the MLSys’21 workshop on “Graph Neural Networks and Systems”, the UCLA’19 workshop on “New Deep Learning Techniques”, and the NeurIPS’17, CVPR’17 and SIAM’18 tutorials on “Geometric Deep Learning on Graphs and Manifolds”. He has been a speaker at the top machine learning conferences KDD’21, AAAI’21, ICLR’20 and ICML’20. In 2002, he co-developed with Turing award winner Yoshua Bengio a new class of expressive GNN. He has published more than 70 peer-reviewed articles, which have been cited 13,500+. A/Prof Bresson has been the main lecturer of undergraduate deep learning courses since 2015 at EPFL, NTU and NUS, and has received outstanding evaluations for his teaching material and engaging style. He has also taught graduate courses on advanced deep learning at NUS and was a guest lecturer for Turing award winner Yann LeCun’s course at NYU. He has provided industrial short courses on AI and Deep Learning to Fortune 500 companies s.a. Deloitte, UnitedHealth and university alumni training centres at UCLA, EPFL, and NTU.

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Programme Fee S$2,100.00 S$2,100.00 S$2,100.00 S$2,100.00 S$2,100.00
    Less: SSG Grant Amount S$1,470.00 S$1,470.00 S$1,470.00 S$1,470.00 -
    Nett Programme Fee S$630.00 S$630.00 S$630.00 S$630.00 S$2,100.00
    9% GST on Nett Programme Fee S$56.70 S$56.70 S$56.70 S$56.70 S$189.00
    Total Nett Programme Fee Payable, Including GST S$686.70 S$686.70 S$686.70 S$686.70 S$2,289.00
    Less Additional Funding - S$420.00 - S$420.00 -
    Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$686.70 S$266.70 S$686.70 S$266.70 S$2,289.00
    1. Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
    2. GST shall apply at prevailing rates.
    3. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.

    Other Information

    This course is eligible for Union Training Assistance Programme (UTAP). NTUC members can enjoy up to 50% funding (capped at $250 per year) under UTAP. NTUC members aged 40 and above can enjoy higher funding support up to $500 per individual each year, capped at 50% of unfunded course fees, for courses attended between 1 July 2020 to 31 December 2025. Please click here for more information.

    Connect with us

    11 Research Link
    COM 3, #B1-07
    Singapore 119391
    Telephone: +65 6516 5523
    Email: soc-ace@nus.edu.sg

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