# ## 주메뉴

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공학 >전기ㆍ전자 >전자공학
• 강의학기
2019년 2학기
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1,277
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강의계획서 This lecture will cover the basic concepts and principles of pattern recognition and introduce its various applications to help student understand what the pattern recognition is and how it can be used for their research.

#### 차시별 강의      1. Lecture 1: Introduction 1. 강의에 대한 전반적인 내용 설명 - 강의 구성 - 강의 평가방법 2. Pattern Recognition 소개 - main objectives - classification / clustering - applications 2. Lecture 2-1: Probability and Statistics •Probability Theory -Parameter Estimation -Minimum Expectation -Bayes Rule -The Gaussian Distribution -Exponential Family •Probabilistic Decision Theory –Reject option –Minimizing risk -Unbalanced class priors -Combining models  Lecture 2-2: Probability and Statistics •Probability Theory -Parameter Estimation -Minimum Expectation -Bayes Rule -The Gaussian Distribution -Exponential Family •Probabilistic Decision Theory –Reject option –Minimizing risk -Unbalanced class priors -Combining models 3. Lecture 3-1: Bayesian Decision Theory & Cross Validataion •Probability Theory -Bayesian Decision Rule -Maximum a Posteriori decision rule -Maximum Likelihood decision rule –Reject option •Risk Minimization –Minimizing risk -Unbalanced class priors -Combining models •Cross Validation –Comparison of CV and Boostrapping  Lecture 3-2: Bayesian Decision Theory & Cross Validataion •Probability Theory -Bayesian Decision Rule -Maximum a Posteriori decision rule -Maximum Likelihood decision rule –Reject option •Risk Minimization –Minimizing risk -Unbalanced class priors -Combining models •Cross Validation –Comparison of CV and Boostrapping 4. Lecture 4: Normal Random Variable and Its Discriminant Function Designs Normal Random Variable -Properties -Quadratic Discriminant Function Designs Gaussian Mixture Model -GMM Expression 5. Lecture 5: Principal Component Analysis Principal Component Analysis-finds orthonormal basis for data -sorts dimensions in order of importance -discard low significance dimensions 6. Lecture 6: Support Vector Machines The VC dimension -Classifier Margin -Margin Estimation -The Dual Problem 7. Lecture 7-1: Unsupervised clustering Partitional Clustering -Centroid-based clustering -K-means and K-medoids -Gaussian mixture model  Lecture 7-2: Unsupervised clustering Partitional Clustering -Centroid-based clustering -K-means and K-medoids -Gaussian mixture model 8. Lecture 8: Unsupervised clustering(2) Partitional Clustering -Centroid-based clustering -K-means and K-medoids -Gaussian mixture model 9. Lecture 9: Perceptron, Logistic Regression, Multi Layer Perceptron Perceptron -canonical representation -optimization problem -gradient decent search Logistic Regression -maximum likelihood learning 10. Lecture 10: Handwritten Digit(MNIST) Recognition Using Deep Neural Networks MNIST hand written digit data base Neural Networks Autoencoder Softmax Regression Convolutional Neural Networks for MNIST 11. Lecture 11: Dynamic time warping dynamic pattern recognition Dynamic Time Warping Isolated word recognition -metric distance -isolated word recognition with DTW DTW Applications #### 연관 자료 #### 사용자 의견 #### 이용방법

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