Artificial Intelligence and
Machine Learning for Metaverse
Dates: 6 Nov, 7 Nov 2023 | 9am-5.30pm | Classroom Learning
Duration: 2 Days
Course Overview
This course helps learners understand the role of Artificial Intelligence (AI) and Machine Learning (ML) in the foundation and development of the Metaverse. Topics such as supervised machine learning, unsupervised machine learning and deep learning will be covered. Through relevant case studies, learners will get an operational understanding on how these technologies support the operations of the Metaverse.
This course is part of Professional Certificate in Infrastructure of the Metaverse.
Course Objectives
At the end of this course, learners should be able to:
- Understand how AL and ML can be used to analyse the data collected in Metaverse
- Apply Machine Learning (ML) models to solve clustering, classification and regression problems in Metaverse
- Apply Deep Learning (DL) models to develop computer vision and Natural Language Processing (NLP) tasks in Metaverse
- Create a viable business idea that supports the Metaverse using AI and ML technology
Who Should Attend
Executives, Developers, Designers and Managers in IT-related fields, business development, strategic planning, operations.
Prerequisites
Minimum Diploma in IT or related fields.
Course Convener
(Click their photos to view their short 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.
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 Codes
TGS-2022015679 (Classroom Learning)
TGS-2022015678 (Synchronous e-learning)
Course Fee Breakdown
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older