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Introduction
Recent developments in the field of AI have fostered multidisciplinary research in various disciplines, including computer science, linguistics, and psychology. Intelligence, in fact, is much more than just IQ: it comprises many other kinds of intelligence, including physical intelligence, cultural intelligence, linguistic intelligence, and EQ. In this course, we are going to the technologies that are referred to as Affective AI or Emotion AI. Affective AI is a subset of artificial intelligence (the broad term for machines replicating the way humans think) that measures, understands, simulates, and reacts to human emotions.
- 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
At the end of the course, learners are able to:
1. understand the difference between kinds of artificial intelligence (e.g., IQ and EQ) and;
2. develop systems and devices that can recognize, interpret, process, and simulate human affects for tasks such as sentiment analysis, social media marketing, financial forecasting, etc.
2. What is not Affective AI?
3. What can Affective AI do for you?
4. Pros & cons of Affective AI
5. Potential benefits
6. Limitations
7. Risks
8. Symbolic vs Sub-symbolic Affective AI
9. Knowledge graphs
10. Deep learning
11. Hybrid Affective AI
12. Knowledge Representation for Affective AI
13. One hot encoding
14. Weighing schemes
15. Embeddings
16. Knowledge Exploitation for Affective AI
17. Semantic similarity
18. Dimensionality reduction
19. Vector quantization and classification
20. Affective AI applications
21. Sentiment analysis
22. Empathetic Dialogue systems
23. Common practices and mistakes
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$1,944
SSG Funding Support | Course fee payable after SSG funding, if eligible under various schemes | ||
Fee BEFORE funding & GST | Fee AFTER funding & 8% GST | ||
Singapore Citizens (SCs) and Permanent Residents (PRs) (Up to 70% funding) | s$1,800.00 | S$583.20 | |
Enhanced Training Support for SMEs (ETSS) | S$223.20 | ||
SCs aged ≥ 40 years old | |||
• 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 |
| CET807 Application 2: Introduction to Computer Vision | 1 |
| CET808 Application 3: Introduction to Cloud AI | 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.
