Dates: To be advised
Duration: 2 Days
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.
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.
Basic Python programming skills
(Click photo to view biography)
Assoc 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.
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email firstname.lastname@example.org
To register, click Register
TGS-2023022211 (Synchronous e-Learning)
Course Fee Breakdown
Singapore Citizens39 years old or younger
Singapore Citizen40 years old or older