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Introduction
The success of AI comes from the high-performance accelerators and big data. As cloud platforms can provide almost endless computing resources and store massive amounts of data, it is very natural to leverage them to support AI applications. Therefore, in recent years, both industry and academia have invested a lot of effort to build AI infrastructure in clouds to ease the development and deployment of AI applications.
In this course, learners will learn the cutting-edge knowledge about cloud computing and AI as well as master the recent techniques that streamline the AI applications development in clouds. After hands-on exercises, learners will build, test and deploy their own Cloud AI applications to provide intelligent services for end-users.
- Graduate Certificate in Data Science and Artificial Intelligence
- Graduate Certificate in Artificial Intelligence and AI Plus
- FlexiMasters
in Data Science and Artificial Intelligence
- FlexiMasters in Artificial Intelligence and AI Plus
Download Learning Pathway e-Guide
At the end of the course, learners are able to:
1. Learn the basic AI knowledge (e.g., deep neural network) and use Python to implement train and test AI models;
2. Acquire the basic knowledge of cloud computing and build AI applications with the SDKs provided by the main cloud platforms and;
3. Develop and deploy AI applications (e.g. video analysis) on AWS by using SageMaker.
Machine Learning & Deep Learning principles
- Introduction and basic concepts
- Case studies with the main models (ResNet50, GAN, etc)
- Hands-on Deep Learning development for the main algorithms (CNN, LSTM, etc)
- Cloud Computing principles
- Background and cloud platform introduction (AWS, Google Cloud Platform, and
Azure Platform)
- Hands-on AI application development with APIs provided by the main cloud
platforms
Cloud-native AI application development
- MLOps: Train, test, and deploy Deep Learning models using containers on a cloud
server
- Hands-on end-to-end cloud AI applications development and deployment using
AWS SageMaker
For learner who wish to acquire the knowledge and skills to boost their career prospects, become savvier in technology applications as well as better equipped for the fast paced advancements expected ahead.
Standard Course Fee: S$1,962
SSG Funding Support | Course fee | Course fee payable after SSG funding, if eligible under various schemes | |
BEFORE funding & GST | AFTER funding & 9% GST | ||
Singapore Citizens (SCs) and Permanent Residents (PRs) (Up to 70% funding) | S$1,800 | S$588.60 | |
Enhanced Training Support for SMEs (ETSS) | S$228.60 | ||
SCs aged ≥ 40 years old | |||
- Standard course fee is inclusive of GST.
- NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.
| COURSE TITLE | ACADEMIC UNIT |
| CET787 Foundations of Computation Thinking and Programming | 1 |
| CET788 AI 1:AI Foundation | 1 |
| CET789 AI 2: Reinforcement Learning | 1 |
| CET790 AI 3: Computational Game Theory | 1 |
| CET791 ML1: Supervised learning: Bayesian decision theory and classifiers | 1 |
| CET792 ML2: Supervised learning: Non-probabilistic classifiers | 1 |
| CET793 ML3: Unsupervised learning | 1 |
| CET794 AI Ethics 1: Foundations of AI Ethics | 1 |
| CET795 AI Ethics 2: AI Ethic Standardization | 1 |
| CET800 AI Ethics 3: Ethics in Data Processing | 1 |
| CET802 DS1: Descriptive Analytics | 1 |
| CET803 DS2: Predictive Analytics | 1 |
| CET804 DS3: Pattern Recognition | 1 |
| CET806 Application 1: Introduction to Affective AI | 1 |
| CET807 Application 2: Introduction to Computer Vision | 1 |
Listed courses are:
- Credit-bearing and stackable to Graduate Certificate in Data Science and Artificial Intelligence (total 9AUs), and FlexiMasters in Data Science and Artificial Intelligence (total 15AUs).
- SkillsFuture Credit approved.
