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Short Course

Advanced Deep Learning

Course ScheduleDuration (days)Mode of DeliveryTGS Reference Number
24 and 25 February 2026 | 9 am - 5.30 pm2Classroom LearningTGS-2025056698

Course Overview

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders, and Deep Reinforcement Learning, are creating impressive results in a series of traditional and creative applications. For learners with basic deep learning and Python programming knowledge, it is critical to complete their knowledge towards implementation of deep learning systems in the real-world ecosystems, as well as to have more information about advanced deep learning models, how they work, what are their advantages, and applications. Again, since the implementation of robust natural language processors is one of the abilities of deep neural networks, and Natural Language Processing (NLP) has got many applications in a diverse set of businesses, practicing that application of deep learning is advisable and rational.

Course Objectives

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

  • Articulate advanced deep learning models, their strengths and constraints, and their applications.
  • Cultivate advanced deep learning systems in different businesses.
  • Have basic skills in using deep learning algorithms to implement simple Natural Learning Processing (NLP) systems.

Job Role Readiness

ICT executives, managers, engineers and analysts.

Prerequisites

Basic deep learning knowledge, and basic Python programming skills. Click to view the course detail of Python Programming.

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This programme is part of

  • Professional Certificate in Text Processing

    • Funding Available

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

    • Testimonials


    Professor Xavier Bresson's deep knowledge and passionate delivery made the course engaging. The slides were well-structured, clear, and easy to follow, with excellent visual aids. The teaching style was inclusive and responsive to questions. This course exceeded my expectations, and I highly recommend it for anyone.

    Instructors


    Dr Ai Xin

    Dr Ai Xin

    Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience teaching Artificial Intelligence and Data Science courses such as machine learning, deep learning and data mining. She graduated from NUS with a PhD degree in Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks. She worked at BHP Billiton Marketing Asia for eight years, gaining extensive industry experience in various functions such as risk management, supply chain management, and sales and marketing planning.

    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.

    Course Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Course Fee S$2,100.00 S$2,100.00 S$2,100.00 S$2,100.00 S$2,100.00
    9% GST on Full Course Fee S$189.00 S$189.00 S$189.00 S$189.00 S$189.00
    SSG Funding S$1,050.00 S$1,050.00 S$1,050.00 S$1,050.00 -
    Additional Funding if Eligible under Various Schemes - S$420.00 - S$420.00 -
    Total Nett Course Fee Payable, including GST S$1,239.00 S$819.00 S$1,239.00 S$819.00 S$2,289.00
    1. Total nett course fee payable, including GST, after additional funding from various funding schemes.
    2. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
    3. The applicable funding is for Singaporeans and Singapore PRs. This is subject to change and in accordance with SSG funding guidelines as of course commencement.