◎系所教育目標：
為配合國家建設及產業發展之需要，本系以培育中高級資訊科技人才為目的。在教學理念上除了注重理論的探討之外並強調實際動手的能力，以期培育出具有深厚學識基礎並能實際應用的資訊科技人才。在專業必修中涵蓋基礎理論、電腦硬體、作業系統、資料結構及計算機網路等方面，並有畢業專題製作，使學生紮實基礎，同時課程包含四個專業學程，兼顧學術及實務之分流與訓練。分別為一：軟體工程及知識工程學程、二：互動多媒體學程、三：網路及資訊安全學程、四：資訊系統開發實務學程，以期作為日後升學就業的準備。 |
| ◎核心能力 | 關聯性 |
| 1.應用數理邏輯推理之能力 | 3 關聯性中等 |
| 2.具備資訊軟體專業之能力 | 2 關聯性稍弱 |
| 3.發掘、分析及解決問題之能力 | 3 關聯性中等 |
| 4.有效溝通與團隊合作之能力 | 2 關聯性稍弱 |
◎本學科內容概述：
本課程將介智慧電腦系統之基本想法與技術，尤其強調機率統計與決策模型在人工智慧應用上的基本原理。授課內容可以區分為介紹、搜尋演算法、邏輯推理、機率統計之人工智慧方法。 |
◎本學科教學內容大綱：
1. 人工智慧簡介
2. 問題解決與目標狀態搜尋
3. 知識、推論與規劃
4. 不確定性知識與推論 |
◎本學科學習目標：
This course will introduce the basic ideas and techniques about artificial intelligence systems. We will emphasis on the statistical and decision-theoretic modeling in computer science. In this course, the students will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings for the environments. The agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. The machine learning algorithms including deep learning algorithms will be introduce here. The techniques those are learned in this course be apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue. |
| ◎教學進度： |
| 週次 | 主題 | 教學內容 | 教學方法 |
01
02/24 | Introduction
Project assignment | 1. Introduction to this course
2. Evauations and course policy
P0: Initial (Team organization and Project assignment) | 講授、討論。 |
02
03/03 | Basic Concepts about AI | 1. Intoduction to AI
2. Pre-history and history of AI
3. AI and related areas | 講授、討論。 |
03
03/10 | Intelligent Agent | 1. Definition of agents
2. Classification about agents and environments | 講授、討論。 |
04
03/17 | Search | 1. Search algorithm
P1. Presentation for project planning, risk analysis and engineering (1.Spec. and user interface design-1/2) | 口頭報告、講授、討論。 |
05
03/24 | Search | 1. Tree search algorithm
P2. Presentation for project planning , risk analysis and engineering (1.Spec. and user interface design-2/2) | 講授、討論。 |
06
03/31 | Search | 1. Graph search
P3. Presentation for project evaluation (1.Spec. and user interface design-1/3)
P4. Presentation for project evaluation (1.Spec. and user interface design-2/3) | 講授、討論。 |
07
04/07 | Heuristics | 1. Heuristics
2. A*
P5. Presentation for project evaluation (1.Spec. and user interface design-1/2) | 講授、討論。 |
08
04/14 | Midterm exam. | Midterm exam. | 講授、討論、考試。 |
09
04/21 | Uncertenty | 1. Uncertainty
2. Probability | 操作/實作、口頭報告、講授、討論。 |
10
04/28 | Bayes Network | 1. Causaulity Analysis
2. Definition of Bayes Network
3. Bayes Network Inference | 講授、討論。 |
11
05/05 | Markov Chain | 1. Markov Chain
2. Concepts about the state transition | 講授、討論。 |
12
05/12 | Hidden Morkov Models(1/2)
Project assignment | 1. Hidden Morkov Models
P6. Presentation for project planning , risk analysis and engineering (2.Algorithm implementation-1/3)
P7. Presentation for project planning , risk analysis and engineering (2.Algorithm implementation-1/3)
P8. Presentation for project planning , risk analysis and engineering (2.Algorithm implementation-1/3)
P9. Presentation for project evaluation (2.Algorithm implementation-1/2) | 講授、討論。 |
13
05/19 | Hidden Morkov Models
(2/2)
Hidden Morkov Models
Project assignment | 1. Hidden Morkov Models
p10. Presentation for project evaluation (2.Algorithm implementation-2/2)
P13. Presentation for project planning , risk analysis and engineering (3. Evaluation and discussion-1/3) | 講授、討論。 |
14
05/26 | Project assignment | P14. Presentation for project planning , risk analysis and engineering (3. Evaluation and discussion-2/3) | 講授、討論。 |
15
06/02 | Propositional Logic | 1. Proposition and inference
2. Logic
P15. Presentation for project planning , risk analysis and engineering (3. Evaluation and discussion-3/3) | 講授、討論。 |
16
06/09 | Final exam. | Final exam. | 考試。 |
17
06/16 | Final Project | 1. Presntation
2. Project Report
p15. Presentation for project evaluation ((3. Evaluation and discussion-1/2)
p16. Presentation for project evaluation ((3. Evaluation and discussion-2/2) | 口頭報告、講授、討論。 |
18
06/23 | Fist oder logic | 1. Fist oder logic
2. Examples | 講授、討論。 |
◎課程要求：
The students will attend this course should be able to deal with the basic problems about probability, programming and algorithms. |
◎成績考核
課堂參與討論40% : Course attending status
期末考30% : Final exam
書面報告10% : AI Project Assignment
口頭報告20% : AI Project Assignment
|
◎參考書目與學習資源
1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4rd Ed., Prentice Hall. （新月代理）
2. Joseph Giarratano and Gary Riley, Expert Systems Principles and Programming, Third Ed., PWS Publishing Co., 1998.（開發代理）
3. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, Second Ed., Addison Wesley, 2004.（全華代理）
4. Ben Coppin, Artificial Intelligence illuminated. Jones and Bartlett Publishers.（開發代理） |
| ◎教材講義 請改以帳號登入校務系統選擇全校課程查詢方能查看教材講義 |