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Introduction
Popularized by the movie A Beautiful Mind, game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Beyond what we call `games' in common language, such as chess, poker, soccer, etc., game theory includes the modeling of conflict among nations, political campaigns, competition among firms, and trading behavior in markets such as the New York Stock Exchange. How could you begin to model keyword auctions, and peer-to-peer file-sharing networks, without accounting for the incentives of the people using them? The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more. We will include a variety of examples including classic games and a few applications such as its application to security.
- Graduate Certificate in Artificial Intelligence and AI Ethics
- Graduate Certificate in Data Science and Artificial Intelligence
- Graduate Certificate in Artificial Intelligence and AI Plus
- FlexiMasters in Artificial Intelligence and AI Ethics
- FlexiMasters in Data Science and Artificial Intelligence
- FlexiMasters in Artificial Intelligence and AI Plus
Download Learning Pathway e-Guide
Course Availability
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Date(s): 04 to 11 May 2024
Time: Live online sessions on two Saturdays (9.30am-11.30am). Live E-consultation on one Wednesday (7.30pm-9.30pm).
Venue: Live online
Registration is closed.
1. Understand the fundamental concepts of game theory, in particular standard game models and solution concepts;
2. Understand a variety of algorithmic techniques for computing game-theoretic solution concepts (equilibria);
3. Apply solution concepts and algorithms to unseen games that are variants of known examples and;
4. Understand the state of the art in some areas of algorithmic research, including new developments and open problems.
1. Game models: Strategic form, extensive form, games of incomplete information (e.g., auctions), succinct representations, co-operative games;
2. Solution concepts: Nash equilibria, subgame perfection, correlated equilibria, Bayesian equilibria, core and Shapley value;
3. Finding equilibria: Linear programming algorithms and;
4. Application of game theory to the real world.
For learner who wish to acquire more knowledge in applying AI practices in organizations and, to understand and help societies to solve problems brought about by the impact of AI.
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.
Artificial Intelligence and AI Ethics
| COURSE TITLE | ACADEMIC UNIT |
| CET787 Foundations of Computation Thinking and Programming | 1 |
| CET788 AI 1:AI Foundation | 1 |
| CET789 AI 2: Reinforcement Learning | 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 |
| CET807 Application 2: Introduction to Computer Vision | 1 |
| CET797 Body of Knowledge (BoK) for AI Ethics and Governance | 1 |
| CET798 AI Ethics Governance Framework for Organisations | 1 |
| CET799 Business Liability and Ethics in AI Usage | 1 |
| CET800 AI Ethics 3: Ethics in Data Processing | 1 |
| CET801 Governance for AI Explainability | 1 |
Data Science and Artificial Intelligence
| COURSE TITLE | ACADEMIC UNIT |
| CET787 Foundations of Computation Thinking and Programming | 1 |
| CET788 AI 1:AI Foundation | 1 |
| CET789 AI 2: Reinforcement Learning | 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 |
| CET802 DS1: Descriptive Analytics | 1 |
| CET803 DS2: Predictive Analytics | 1 |
| CET804 DS3: Pattern Recognition | 1 |
| CET805 AI Ethics 3: Ethics in Data Processing | 1 |
| CET806 Application 1: Introduction to Affective AI | 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 Artificial Intelligence and AI Ethics, Graduate Certificate in Data Science and Artificial Intelligence (total 9AUs), FlexiMasters in Artificial Intelligence and FlexiMasters in Data Science and Artificial Intelligence (total 15AUs).
- SkillsFuture Credit approved.
