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페이스북 트위터 카카오톡 네이버밴드 링크복사

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공학 >전기ㆍ전자 >전자공학
강의학기

2018년 2학기

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3,230

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 to Pattern Recognition	Introduction to Pattern Recognition
	Lecture 1: Introduction to Pattern Recognition	Introduction to Pattern Recognition
2.	Lecture 2: Probability Theory and Probabilistic Decision Theory	Basic Probability Theory
	Lecture 2: Probability Theory and Probabilistic Decision Theory	Basic Probability Theory
3.	Lecture 3: Bayesian Decision Theory	Bayesian Inference and Decision Theory
	Lecture 3: Bayesian Decision Theory	Bayesian Inference and Decision Theory
4.	Lecture 4: Clustering and K-means Clustering	Clustering Vector Quantization (VQ) Pattern Recognition using VQ
	Lecture 4: Clustering and K-means Clustering	Clustering Vector Quantization (VQ) Pattern Recognition using VQ
	Lecture 4: Clustering and K-means Clustering	Clustering Vector Quantization (VQ) Pattern Recognition using VQ
5.	Lecture 5: Normal Random Variable and Its Discriminant Function Designs	Normal Distributions
	Lecture 5: Normal Random Variable and Its Discriminant Function Designs	Normal Distributions
	Lecture 5: Normal Random Variable and Its Discriminant Function Designs	Normal Distributions
6.	Lecture 6: Gaussian Mixture Models and Cross Validation	Gaussian Mixture Models (GMM)
	Lecture 6: Gaussian Mixture Models and Cross Validation	Gaussian Mixture Models (GMM)
	Lecture 6: Gaussian Mixture Models and Cross Validation	Gaussian Mixture Models (GMM)
7.	Lecture 7: Support Vector Machines	Support Vector Machines (SVM)
	Lecture 7: Support Vector Machines	Support Vector Machines (SVM)
8.	Lecture 8: Principal Component Analysis	Principal Component Analysis
	Lecture 8: Principal Component Analysis	Principal Component Analysis
9.	Lecture 9: Single-Layer Linear Perceptron and Multi-Layer Perceptron	Single-Layer Linear Perceptron and Multi-Layer Perceptron
	Lecture 9: Single-Layer Linear Perceptron and Multi-Layer Perceptron	Single-Layer Linear Perceptron and Multi-Layer Perceptron
10.	Lecture 10: Handwritten Digit(MNIST) Recognition Using Deep Neural Networks 1	Handwritten Digit(MNIST) Recognition Using Deep Neural Networks 1
	Lecture 10: Handwritten Digit(MNIST) Recognition Using Deep Neural Networks 1	Handwritten Digit(MNIST) Recognition Using Deep Neural Networks 1
	Lecture 10: Handwritten Digit(MNIST) Recognition Using Deep Neural Networks 2	Handwritten Digit(MNIST) Recognition Using Deep Neural Networks 2
	Lecture 10: Handwritten Digit(MNIST) Recognition Using Deep Neural Networks 2	Handwritten Digit(MNIST) Recognition Using Deep Neural Networks 2