課程名稱 |
(中文) 圖形識別 (英文) Pattern Recognition |
開課單位 | 資訊工程研究所 | ||
課程代碼 | I4560 | ||||
授課教師 | 虞台文 | ||||
學分數 | 3.0 | 必/選修 | 選修 | 開課年級 | 研究所 |
先修科目或先備能力:Linear Algebra, Probability | |||||
課程概述與目標: 圖形識別為智慧型系統重要的一個單元,為各種辨識技術,如:電腦視覺、語音辨識、生物辨識等,的主要基礎。主要授課內容包括:各種識別技術、特徵擷取方法、與機器學習等。本課程對未來從事各項智慧型系統的深入研究,有莫大幫助。 | |||||
教科書 | Pattern Classification (2nd Edition) Richard O. Duda, Peter E. Hart, and David G. Stork |
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參考教材 | Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition, by David G. Stork, Elad Yom-Tov |
課程大綱 | 學生學習目標 | 單元學習活動 | 學習成效評量 | 備註 | ||
週 | 單元主題 | 內容綱要 | ||||
1 | Introduction | 1. The goal of pattern classification 2. The main components 3. Some practical examples |
Introduction |
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2 | Bayesian Decision Theory | 1. Bayesian Decision Theory 2. Classifiers, Discriminant Functions, and Decision Surfaces |
Bayesian Decision Theory |
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3 | Bayesian Decision Theory | 3. The Normal Density 4. Discriminant Functions for the Normal Density |
Bayesian Decision Theory |
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4 | Maximum Likelihood | 1. Maximum Likelihood Estimation 2. Baysian Estimation |
Maximum Likelihood |
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5 | Bayesian Parameter Estimation | 1. Bayesian Parameter Estimation: Gaussian Case 2. Bayesian Parameter Estimation: General Theory |
Bayesian Parameter Estimation |
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6 | Hidden Markov Models | 1. Hidden Markov Models 2. Evaluation, Decoding, and Learning |
Hidden Markov Models |
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7 | Nonparametric Techiques | 1. Density Estimation 2. Parzen window 3. Nearest-Neighbor Estimation |
Nonparametric Techiques |
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8 | Nonparametric Techiques | 4. Nearest-Neighbor Rule 5. Metrics and Nearest-Neighbor Classification |
Nonparametric Techiques |
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9 | Review | Review | Mid-term |
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10 | Linear Discriminant Functions | 1. Linear Discriminant Functions and Decision Surfaces 2. Two-Category Linearly Separable Case 3. Minimizing the Perceptron Criterion Function |
Linear Discriminant Functions |
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11 | Linear Discriminant Functions | 4. Relaxation Procedures 5. Minimum Squared-Error Procedures |
Linear Discriminant Functions |
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12 | Multilayer Neural Networks | 1. Feedforward Operation and Classificaion 2. Backpropagation Algorithm 3. Error Surface |
Multilayer Neural Networks |
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13 | Multilayer Neural Networks | 4. Backpropagation as Feature Mapping 5. Backpropagation, Bayes Theory and Probability 6. Practical Techniques for Improving Backpropagaion |
Multilayer Neural Networks |
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14 | Stochastic Methods | 1. Stochastic Search 2. Boltzmann Learning |
Stochastic Methods |
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15 | Nonmetric Methods | 1. Decision Trees 2. CART |
Nonmetric Methods |
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16 | Nonmetric Methods | 3. Grammatical Methods 4. Grammatical Inference |
Nonmetric Methods |
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17 | Unsupervised Learning and Clustering | 1. Unsupervised Bayesian Learning 2. Data Description and Clustering |
Unsupervised Learning and Clustering |
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18 | Review | Review | Final |
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教學要點概述: |