Dates: 24 Oct, 25 Oct 2023 | 9am-5.30pm | Classroom Learning
Duration: 2 Days
Course Overview
This course will provide students with a basic understanding of generative Artificial Intelligence (AI) models, their applications, and the underlying techniques used to create them. Students will learn about various generative models, such as Generative adversarial networks (GANs), Variational autoencoders (VAEs), and Transformer-based architectures like Generative Pre-trained Transformer (GPT). They will gain hands-on experience in implementing these models using popular frameworks, and apply them to a variety of tasks, including image synthesis, text generation, and creative AI applications.
This course is part of Professional Certificate in ChatGPT, Advanced Chat Models, and Generative AI.
Course Objectives
By the end of the course, students will be able to:
- Understand the principles and concepts behind generative AI models, including GANs, VAEs and Transformer-based architectures like GPT
- Apply the knowledge gained to implement generative models using popular frameworks.
Prerequisites
Basic programming skills
Course Conveners
(Click photos to view biographies)
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 on teaching Artificial Intelligence and Data Science courses, e.g. machine learning, deep learning, data mining and etc.
She graduated from NUS with a PhD degree on Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks.
She worked in BHP Billiton Marketing Asia for eight years and gained a lot of industry experience through different functions, e.g. risk management, supply chain management, sales and marketing planning and etc.
Dr Edmund Low
Dr Edmund Low
Dr Edmund Low is currently Senior Lecturer with the NUS College at the National University of Singapore.
He has nearly 20 years of academic and professional experience in the use of data-driven tools to answer questions in public health and the environment. His past projects include applying AI techniques and machine learning models for environmental modelling and impact assessment. He currently heads the quantitative reasoning domain at USP, and teaches courses on statistical methods, data science and machine learning. As an educator, Edmund is a multiple recipient of both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. Edmund holds a PhD in Environmental Engineering from Yale University.
Course Fees
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 soc-ace@nus.edu.sg
To register, click Register
Course Code
TGS-2023022203(Classroom Learning)
Course Fee Breakdown
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older