# ## 주메뉴

### 형태인식론

• 경북대학교
• 장길진 • 주제분류
공학 >전기ㆍ전자 >전자공학
• 강의학기
2018년 2학기
• 조회수
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 #### 연관 자료 #### 사용자 의견 #### 이용방법

• 문서 자료 이용시 필요한 프로그램 [바로가기]

※ 강의별로 교수님의 사정에 따라 전체 차시 중 일부 차시만 공개되는 경우가 있으니 양해 부탁드립니다.