1. |
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Lecture 1: Introduction
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1. 강의에 대한 전반적인 내용 설명
- 강의 구성
- 강의 평가방법
2. Pattern Recognition 소개
- main objectives
- classification / clustering
- applications |
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2. |
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Lecture 2-1: Probability and Statistics
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•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
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•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 |
![URL](/home/images/search/btnUrl.gif) |
3. |
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Lecture 3-1: Bayesian Decision Theory & Cross Validataion
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•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 |
![URL](/home/images/search/btnUrl.gif) |
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![문서 문서](/home/images/search/ico_pdf.gif) |
Lecture 3-2: Bayesian Decision Theory & Cross Validataion
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•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 |
![URL](/home/images/search/btnUrl.gif) |
4. |
![문서 문서](/home/images/search/ico_pdf.gif) |
Lecture 4: Normal Random Variable and Its Discriminant Function Designs
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Normal Random Variable
-Properties
-Quadratic Discriminant Function Designs
Gaussian Mixture Model
-GMM Expression |
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5. |
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Lecture 5: Principal Component Analysis
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Principal Component Analysis-finds orthonormal basis for data
-sorts dimensions in order of importance
-discard low significance dimensions |
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6. |
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Lecture 6: Support Vector Machines
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The VC dimension
-Classifier Margin
-Margin Estimation
-The Dual Problem |
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7. |
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Lecture 7-1: Unsupervised clustering
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Partitional Clustering
-Centroid-based clustering
-K-means and K-medoids
-Gaussian mixture model |
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Lecture 7-2: Unsupervised clustering
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Partitional Clustering
-Centroid-based clustering
-K-means and K-medoids
-Gaussian mixture model |
![URL](/home/images/search/btnUrl.gif) |
8. |
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Lecture 8: Unsupervised clustering(2)
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Partitional Clustering
-Centroid-based clustering
-K-means and K-medoids
-Gaussian mixture model |
![URL](/home/images/search/btnUrl.gif) |
9. |
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Lecture 9: Perceptron, Logistic Regression, Multi Layer Perceptron
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Perceptron
-canonical representation
-optimization problem
-gradient decent search
Logistic Regression
-maximum likelihood learning |
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10. |
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Lecture 10: Handwritten Digit(MNIST) Recognition Using Deep Neural Networks
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MNIST hand written digit data base
Neural Networks
Autoencoder
Softmax Regression
Convolutional Neural Networks for MNIST |
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11. |
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Lecture 11: Dynamic time warping dynamic pattern recognition
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Dynamic Time Warping
Isolated word recognition
-metric distance
-isolated word recognition with DTW
DTW Applications |
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