바로가기

주메뉴

형태인식론

  • 경북대학교
  • 장길진
  • 공유하기
  • 강의담기
  • 오류접수
  • 이용안내
  • 주제분류
    공학 >전기ㆍ전자 >전자공학
  • 강의학기
    2019년 2학기
  • 조회수
    859
  •  
강의계획서
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

PDF VIDEO SWF AUDIO DOC AX
1. Lecture 1: Introduction 1. 강의에 대한 전반적인 내용 설명 - 강의 구성 - 강의 평가방법 2. Pattern Recognition 소개 - main objectives - classification / clustering - applications URL
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 URL
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 URL
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 URL
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 URL
4. Lecture 4: Normal Random Variable and Its Discriminant Function Designs Normal Random Variable -Properties -Quadratic Discriminant Function Designs Gaussian Mixture Model -GMM Expression URL
5. Lecture 5: Principal Component Analysis Principal Component Analysis-finds orthonormal basis for data -sorts dimensions in order of importance -discard low significance dimensions URL
6. Lecture 6: Support Vector Machines The VC dimension -Classifier Margin -Margin Estimation -The Dual Problem URL
7. Lecture 7-1: Unsupervised clustering Partitional Clustering -Centroid-based clustering -K-means and K-medoids -Gaussian mixture model URL
Lecture 7-2: Unsupervised clustering Partitional Clustering -Centroid-based clustering -K-means and K-medoids -Gaussian mixture model URL
8. Lecture 8: Unsupervised clustering(2) Partitional Clustering -Centroid-based clustering -K-means and K-medoids -Gaussian mixture model URL
9. Lecture 9: Perceptron, Logistic Regression, Multi Layer Perceptron Perceptron -canonical representation -optimization problem -gradient decent search Logistic Regression -maximum likelihood learning URL
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 URL
11. Lecture 11: Dynamic time warping dynamic pattern recognition Dynamic Time Warping Isolated word recognition -metric distance -isolated word recognition with DTW DTW Applications URL

연관 자료

loading..

사용자 의견

강의 평가를 위해서는 로그인 해주세요.

이용방법

  • 문서 자료 이용시 필요한 프로그램 [바로가기]

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

이용조건