人工智慧
Artificial Intelligence: Principles and Techniques
學期
112-1
學分
3
學分
當期課號
639005
永久課號
AICA30004
開課單位
智慧科學暨綠能學院
授課教師
連紹宇
校區
歸仁
類別
選修
上課時間表
| 節 | 週一 |
|---|---|
2 09:00–09:50 | 人工智慧 CM212 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
概述
In this course, the fundamental knowledge, technologies and methods of artificial intelligence will be introduced. This course covers six parts, including a preliminary introduction of artificial intelligence, problem-solving, knowledge/reasoning/planning, uncertain knowledge and reasoning, machine learning, and communicating, perceiving, and acting.
評分方式
Mid-term Exam: 30% Final Exam: 30% Homework: 40 %
週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | 1. Announce syllabus 2. Introduction to artificial intelligence |
| 第 2 週 | 1. Solving problems by searching 2. Search in complex environments |
| 第 3 週 | 1. Constraint satisfaction problems 2. Adversarial search and games |
| 第 4 週 | 1. Logical agents 2. First-order logic |
| 第 5 週 | National holiday |
| 第 6 週 | 1. Inference in first-order logic 2. Knowledge representation |
| 第 7 週 | Automated planning |
| 第 8 週 | Mid-term exam |
| 第 9 週 | 1. Quantifying uncertainty 2. Probabilistic reasoning |
| 第 10 週 | 1. Probabilistic reasoning over time 2. Making simple decisions |
| 第 11 週 | 1. Making complex decisions 2. Multiagent decision making |
| 第 12 週 | Probabilistic programming |
| 第 13 週 | 1. Learning from examples 2. Knowledge in learning |
| 第 14 週 | Learning probabilistic models |
| 第 15 週 | Deep learning |
| 第 16 週 | Reinforcement learning |
| 第 17 週 | Final exam |
教科書
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th Edt. Pearson, 2021.
Office Hours
- 地點
- Room 214 in Tsu-Yuan Building
- 時間
- 12:00-14:00 on every Monday
- 聯絡方式
- sylien@nycu.edu.tw