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| ATTRIBUTES | Common Core: Foundations (CTDL) |
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| DESCRIPTION | This course introduces the foundations and applications of Artificial Intelligence for non-specialist students. It emphasizes interdisciplinary perspectives, project-driven practice, and societal implications. Students will gain an understanding of AI background, core technologies (such as natural language processing, computer vision, and AI agents), and their impact on future careers and research. The course combines lectures, interactive classroom discussions, and project-based learning to cultivate creativity, critical thinking, and teamwork. By engaging in real-world projects, students will develop hands-on problem-solving skills and enhance their competitiveness in the digital era. Graded Pass/Fail. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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| L01 (6266) | TBA | TBA | CHEN, Jintai CHEN, Lei HU, Xuming LIU, Hao LIU, Li LIU, Xiaofeng XIA, Jun YUE, Yutao YUEN, Cheuk Yi Kelvin | 300 | 0 | 300 | 0 |
| DESCRIPTION | This course introduces students to economics. It consists of three modules: basics of ecomomics (Module1), microeconomics (Module 2) and macroeconomics (Module 3). Module1 covers the basic principles of economics, supply, demand, market equilibrium, welfare, market failure, and the rationale for government intervention. Module 2 covers the optimal decision of consumers and firms, market power and different market structures, Labor markets and inequality, etc. Module 3 includes measuring aggregate economic activities, consumption and investment, money and financial intermediaries, aggregate demand, aggregate supply and business cycles, macroeconomic stability policy, and economic growth. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L03 (6777) | MoWe 10:30AM - 11:50AM | Rm 102, W1 | YUEN, Cheuk Yi Kelvin | 35 | 0 | 35 | 0 | |
| T03 (6780) | Fr 12:00PM - 12:50PM | Rm 102, W1 | YUEN, Cheuk Yi Kelvin | 35 | 0 | 35 | 0 |
| DESCRIPTION | This course builds on the knowledge of the linear regression models to introduce students advanced statistical methods to analyze survey, administrative and other types of data of interest to quantitative social scientists. The introduction of statistical methods is integrated into research contexts and designs from a holistic framework and bridge quantitative social science and computational social science (data science). Topics include measurement, prediction, causal inference, natural experiment and program evaluation (difference-in-differences, panel data, instrumental variables, regression discontinuity), applied to both survey and big data. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6454) | Th 01:30PM - 04:20PM | Rm 239, E1 | YUEN, Cheuk Yi Kelvin | 30 | 0 | 30 | 0 |