人工智慧導論
Introduction to Artificial Intelligence
| 節 | 週四 |
|---|---|
5 13:20–14:10 | 人工智慧導論 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 introduces students to the fundamentals, problem-solving methods, and learning paradigms of artificial intelligence. Topics covered include intelligent agents, uninformed and informed searching, adversarial search and games, statistical learning, neural networks, and AI applications.
課堂作業包括程式作業,修課學生需具備程式撰寫能力以及演算法的基本知識。
遠距上課位置:https://www.youtube.com/@WeiTaChu 課程網頁:http://mmcv.csie.ncku.edu.tw/~wtchu/courses/2024f_AI/
• Five assignments (40%): Including programming, writing report, and short video • One exam (30%) • One final project (30%): Including project proposal, project implementation, writing report, and oral presentation
| 週次 | 主題 |
|---|---|
| 第 1 週 | 本課程為成大主導授課課程,從9/11為成大的第一週授課(與本校上課週次顯示不同),請以上課日期為準。 |
| 第 2 週 | 成大W1 Introduction, Intelligent Agents |
| 第 3 週 | 成大W2 Intelligent Agents hw1公布 (Project分組、主題方向制定) |
| 第 4 週 | 成大W3 Solving Problems by Searching |
| 第 5 週 | 成大W4 Search in Complex Environments hw1繳交、hw2公布 |
| 第 6 週 | 成大W5 Search in Complex Environments |
| 第 7 週 | 成大W6 Quantifying Uncertainty hw2繳交、hw3公布(Project期中報告) |
| 第 8 週 | 成大W7 Learning from Examples |
| 第 9 週 | 成大W8 Learning Probabilistic Models hw3繳交, hw4公布 |
| 第 10 週 | 成大W9 Learning Probabilistic Models |
| 第 11 週 | 成大W10 Deep Learning hw4繳交, hw5公布(final project short video) |
| 第 12 週 | 成大W11 Deep Learning |
| 第 13 週 | 成大W12 Deep Learning for Natural Language Processing hw5繳交 |
| 第 14 週 | 成大W13 Computer Vision |
| 第 15 週 | 成大W14 Final Exam (同時段同步考試) |
| 第 16 週 | 成大W15 Final project報告 (優選團隊、線上線下同步報告) |
| 第 17 週 | 成大W16 放假 |
| 第 18 週 | 成大W17 放假 |
| 第 19 週 | 成大W18 彈性學習 |
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach 4th edition, Pearson,2020.