人工智能於專案管理之應用
Applications of Artificial Intelligence in Project Management
| 節 | 週四 |
|---|---|
5 13:20–14:10 | 人工智能於專案管理之應用 EB118 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
This course aims at teaching student to develop and apply AI techniques in solving problems in the civil engineering and construction management. After completing the course, students are expected to gain skills and knowledge of selecting an appropriate AI model for their concerned problem, including: • Data collection and processing • Develop an advanced AI model to resolve a specific problem • Assess the performance of an AI model
None
Students will be provided notes and other documents before the weekly class begins.
Homework and Quiz: 40% Mid-term report: 20% Final Report: 40%
| 週次 | 主題 |
|---|---|
| 第 1 週 | • Course Introduction • Artificial Intelligence |
| 第 2 週 | • Machine Learning • Data processing |
| 第 3 週 | • K-nearest neighbors algorithm • K-means clustering |
| 第 4 週 | • Linear Regression and Regularization L1/ L2 • LASSO, Ridge and Stepwise Regression |
| 第 5 週 | • Cross-validation • Model Evaluation index • Over-fitting / Under-fitting |
| 第 6 週 | • Locally weighted and Logistic regression |
| 第 7 週 | • Neural Network - Backpropagation Method |
| 第 8 週 | • Classification and Regression Tree (CART) |
| 第 9 週 | • Prediction problems in Civil Engineering and Construction management by Guest Lecturer (tentatively on-class) • Instruction of selecting final project topic |
| 第 10 週 | • Mid-term report (1) |
| 第 11 週 | • Mid-term report (2) |
| 第 12 週 | • Ensemble models and Hybrid models - Bagging / Boosting / Stacking |
| 第 13 週 | • Ensemble models and Hybrid models - XGBoost / Random Forest |
| 第 14 週 | • Team Project - Final Report (1) |
| 第 15 週 | • Team Project - Final Report (2) |
| 第 16 週 | • Machine learning competition • Awards |
Artificial Intelligence: A Modern Approach, Global Edition by Peter Norvig (Author), Stuart Russell (Author) - ISBN 978-1292401133.