형태인식론
- 경북대학교
- 장길진
페이스북
트위터
카카오톡
네이버밴드
링크복사
-
- 주제분류
- 공학 >전기ㆍ전자 >전기전자공학
-
- 강의학기
- 2017년 2학기
-
- 조회수
- 3,417
-
- 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
| 1. | |
Basic Probability Theory | Basic Probability Theory |  |
| 2. | |
Basic Probability Theory | Basic Probability Theory | |
| 3. | |
Bayesian Inference and Decision Theory | Bayesian Inference and Decision Theory | |
| 4. | |
Pattern Recognition and Bayesian Decision Theory | Pattern Recognition and Bayesian Decision Theory | |
|
Logistic-Regression | |
||
| 5. | |
Perceptron and SVM for Pattern recognition | Perceptron and SVM for Pattern recognition | |
| 6. | |
Support Vector Machines | Support Vector Machines | |
|
Support Vector Machines | Support Vector Machines | |
|
| 7. | |
Principle Component Analysis (PCA) | Principle Component Analysis (PCA) | |
| 8. | |
Unsupervised Learning | Unsupervised Learning | |
|
Unsupervised Learning | Unsupervised Learning | |
|
|
Unsupervised Learning | Unsupervised Learning | |
|
| 9. | |
Gaussian mixture model | Gaussian mixture model | |
| 10. | |
Deep neural networks | Deep neural networks | |
| 11. | |
Dynamic time warping | Dynamic time warping | |
| 12. | |
Recurrent Neural Networks | Recurrent Neural Networks | |
연관 자료
