人工智慧時代的資料導向決策科學
Data-Driven Decision Science in the AI Era
| 節 | 週一 |
|---|---|
5 13:20–14:10 | 人工智慧時代的資料導向決策科學 A701（光復） 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
By the end of this course, students will be able to: • Orchestrate Multi-Model Frameworks: Efficiently manage and evaluate various statistical learning architectures to support organizational objectives. • Master Decision Diagnostics: Apply Statistical Learning principles to identify hidden biases and variances in complex data outputs. • Execute Strategic Decision Making: Convert technical predictions into actionable outcomes, emphasizing the human "judgment call." • Evaluate Algorithmic Integrity: Audit the reliability of automated systems through the lens of empirical logic and practical risk management.
This course provides a high-level framework for integrating Statistical Learning with strategic Decision Making in the age of artificial intelligence. Unlike traditional computational courses that focus on manual algorithmic implementation, this curriculum prioritizes "Strategic Command." We focus on the transition from predictive modeling to empirical determination. By utilizing advanced computational diagnostics, we prioritize the more critical challenges of model interpretation, risk assessment, and the exercise of human judgment in high-stakes organizational environments.
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Attendance Requirement: Any student who is absent more than twice during the entire semester will receive a failing grade for this course. Class Participation: Active participation is essential and will be part of your grade. There will be random classroom activities throughout the semester, and your participation in these activities will count toward your attendance and participation grades.
Homework Assignments: 40% Class Participation and Attendance: 10% Midterm Exam: 20% Final Project: 30%
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| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction AI and Data-Driven Decision Making |
| 第 2 週 | Statistical Learning and Decision Framing |
| 第 3 週 | Data Preprocessing and Automated Data Diagnostics |
| 第 4 週 | Linear Regression |
| 第 5 週 | Classification Models |
| 第 6 週 | Model Evaluation |
| 第 7 週 | Model selection and regularization |
| 第 8 週 | Tree‑Based Models |
| 第 9 週 | Midterm Exam |
| 第 10 週 | Tree‑Based Models |
| 第 11 週 | Ensemble Models (Bagging, Boosting) |
| 第 12 週 | Discovery & Anomaly Detection Unsupervised |
| 第 13 週 | Model Risk and Failure |
| 第 14 週 | Explainable AI (Model Interpretation) |
| 第 15 週 | Final Project |
| 第 16 週 | Final Project |
James, G., Witten, D., Hastie, T., Tibshirani, R. ＆amp; Taylor, J. (2023) An introduction to Statistical Learning with Applications in Python. Springer
- 地點
- 教師未提供此項資料
- 時間
- 事先以email約時間
- 聯絡方式
- paulachen@nycu.edu.tw