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 |
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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 |
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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) |
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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) |
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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) |
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6 | Machine Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
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7 | Machine Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
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8 | Machine Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
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9 | Machine Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
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10 | Machine Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
1. Superwised Learning
2. Unsupervise Learning
3. Reinforcement Learning |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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17 | Case Studies |
1. Sudoku
2. Hearts Game |
1. Sudoku
2. Hearts Game |
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18 | Case Studies |
1. Sudoku
2. Hearts Game |
1. Sudoku
2. Hearts Game |
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