| 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 | |
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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 | |
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| 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 | |
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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 | |
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| 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 | |
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Lecture 7-2: Unsupervised clustering | Partitional Clustering -Centroid-based clustering -K-means and K-medoids -Gaussian mixture model | |
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| 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 | |
대구광역시 동구 동내로 64(동내동 1119) 우)41061
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