SDSC6027 - Topics of AI for Computational Social Sciences | ||||||||
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| * The offering term is subject to change without prior notice | ||||||||
Course Aims | ||||||||
This course provides students with an extensive exposure to causal inference, causal machine learning, and selected topics in natural language processing and reinforcement learning, with a heavy emphasis on practical applications. Topics include potential outcome, matching, regression discontinuity, instrumental variables, difference-in-differences, synthetic control, topic modeling, multi-armed bandit. | ||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||
Continuous Assessment: 65% | ||||||||
Examination: 35% | ||||||||
Examination Duration: 2 hours | ||||||||
Min. Examination Passing Requirement: 30% | ||||||||
Detailed Course Information | ||||||||
| SDSC6027.pdf | ||||||||