教學大綱表 (108學年度 第1學期)
請遵守智慧財產權，勿使用非法影印教科書，避免觸法。
課程名稱
Course Title		 (中文) 智慧型系統
(英文) Intelligent Systems 		開課單位
Departments		 資訊工程研究所
課程代碼
Course No.		 I5830
授課教師
Instructor		 虞台文
學分數
Credit		 3.0 必/選修
core required/optional		 必修 開課年級
Level		 研究所
先修科目或先備能力(Course Pre-requisites)：
課程概述與目標(Course Overview and Goals)：訓練學生利用電腦開發具智慧性的軟體，包括智慧型搜尋演算法、隨機演算法、類神經元網路、模糊邏輯、機器學習等內容。
教科書(Textbook)
參考教材(Reference)
課程大綱 Syllabus 學生學習目標
Learning Objectives		 單元學習活動
Learning Activities		 學習成效評量
Evaluation		 備註
Notes
序
No.		 單元主題
Unit topic		 內容綱要
Content summary
1 Search Algorithms 1. Greedy Search
2. Dynamic Programming
3. Backtracking
4. A* Algorithm 		1. Greedy Search
2. Dynamic Programming
3. Backtracking
4. A* Algorithm		  
2 Search Algorithms 1. Greedy Search
2. Dynamic Programming
3. Backtracking
4. A* Algorithm 		1. Greedy Search
2. Dynamic Programming
3. Backtracking
4. A* Algorithm		  
3 Randomized Algorithms 1. Simulated Annealing
2. Genetic Algorithm
3. Swarm Particle Optimization (PSO)
4. Ant Colony Optimization (ACO) 		1. Simulated Annealing
2. Genetic Algorithm
3. Swarm Particle Optimization (PSO)
4. Ant Colony Optimization (ACO)		  
4 Randomized Algorithms 1. Simulated Annealing
2. Genetic Algorithm
3. Swarm Particle Optimization (PSO)
4. Ant Colony Optimization (ACO) 		1. Simulated Annealing
2. Genetic Algorithm
3. Swarm Particle Optimization (PSO)
4. Ant Colony Optimization (ACO)		  
5 Randomized Algorithms 1. Simulated Annealing
2. Genetic Algorithm
3. Swarm Particle Optimization (PSO)
4. Ant Colony Optimization (ACO) 		1. Simulated Annealing
2. Genetic Algorithm
3. Swarm Particle Optimization (PSO)
4. Ant Colony Optimization (ACO)		  
6 Machine Learning 1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning 		1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning		  
7 Machine Learning 1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning 		1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning		  
8 Machine Learning 1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning 		1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning		  
9 Machine Learning 1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning 		1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning		  
10 Machine Learning 1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning 		1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning		  
11 Combinatorial Optimization 1. NP, NP-Completeness, and NP-Hardness
2. LInear Programming & Integer Programming
3. Heuristic Searches
4. Nerual Networks for Optimization 		1. NP, NP-Completeness, and NP-Hardness
2. LInear Programming & Integer Programming
3. Heuristic Searches
4. Nerual Networks for Optimization		  
12 Combinatorial Optimization 1. NP, NP-Completeness, and NP-Hardness
2. LInear Programming & Integer Programming
3. Heuristic Searches
4. Nerual Networks for Optimization 		1. NP, NP-Completeness, and NP-Hardness
2. LInear Programming & Integer Programming
3. Heuristic Searches
4. Nerual Networks for Optimization		  
13 Combinatorial Optimization 1. NP, NP-Completeness, and NP-Hardness
2. LInear Programming & Integer Programming
3. Heuristic Searches
4. Nerual Networks for Optimization 		1. NP, NP-Completeness, and NP-Hardness
2. LInear Programming & Integer Programming
3. Heuristic Searches
4. Nerual Networks for Optimization		  
14 Factor Ananlysis 1. Principal Component Analysis
2. Independent Component Analysis
3. GMM and HMM 		1. Principal Component Analysis
2. Independent Component Analysis
3. GMM and HMM		  
15 Factor Ananlysis 1. Principal Component Analysis
2. Independent Component Analysis
3. GMM and HMM 		1. Principal Component Analysis
2. Independent Component Analysis
3. GMM and HMM		  
16 Factor Ananlysis 1. Principal Component Analysis
2. Independent Component Analysis
3. GMM and HMM 		1. Principal Component Analysis
2. Independent Component Analysis
3. GMM and HMM		  
17 Case Studies 1. Sudoku
2. Hearts Game 		1. Sudoku
2. Hearts Game		  
18 Case Studies 1. Sudoku
2. Hearts Game 		1. Sudoku
2. Hearts Game		  


教學要點概述：
1.自編教材 Handout by Instructor：
□ 1-1.簡報 Slides
□ 1-2.影音教材 Videos
□ 1-3.教具 Teaching Aids
□ 1-4.教科書 Textbook
□ 1-5.其他 Other
□ 2.自編評量工具/量表 Educational Assessment
□ 3.教科書作者提供 Textbook

成績考核 Performance Evaluation： 期末考：30%   期中考：30%   平時考：40%  

教學資源(Teaching Resources)：
□ 教材電子檔(Soft Copy of the Handout or the Textbook)
□ 課程網站(Website)
課程網站(Website)：http://www.cse.ttu.edu.tw/twyu
扣考規定：https://curri.ttu.edu.tw/p/412-1033-1254.php