패턴 인식 입문

강의상세

이 과정은 패턴인식의 기본 개념과 방법을 고찰 함. 베이지안 결정이론, 베이지안 파라미터 추정, 비 파라미터 추정, 선형 판별 함수, 신경망, 통계적 기계학습 등 패턴인식 분야에 널리 쓰이는 핵심이론들의 개념에 대해 학습함.

Component analysis and discriminants (CH 3) - PCA/LDA (2)

1.	Component analysis and discriminants (CH 3) - PCA/LDA (2)	10. 10. 04
2.	Non-Parametric Techniques (CH4)	2010-10-27
3.	Non-Parametric Techniques (CH4)	2010-11-01
4.	Decision Trees (CH8)	2010-11-03
5.	Decision Trees (CH8) 2-1	2011-11-08
6.	Decision Trees (CH8) 2-2
7.	Neural Networks (CH6) -1	2010-11-10
8.	Neural Networks (CH6)	2010-11-15
9.	Error Estimation, Baggin, Boosting (CH9)	2010-11-17
10.	Error Estimation, Baggin, Boosting (CH9) -1	2010-11-22
11.	Error Estimation, Baggin, Boosting (CH9) -2
12.	Classifier Combination(CH10) -1	2010-11-29
13.	Classifier Combination(CH10) -2
14.	Feature Selection	2010-12-01
15.	Feature Selection	2010-12-06
16.	Unsupervised Learning, Clustering, Multidimensiional Scaling	2010-12-08
17.	Unsupervised Learning, Clustering, Multidimensiional Scaling	2011-12-13
18.	Unsupervised Learning, Clustering, Multidimensiional Scaling	2010-12-15
19.	패턴 인식 강의 소개	10. 8. 30
20.	Statistical Decision Theory (Ch 2)	10. 9. 1
21.	Statistical Decision Theory (Ch 2)	10. 9. 6
22.	Statistical Decision Theory (Ch 2)	10. 9. 8
23.	Statistical Decision Theory (Ch 2)	10. 9. 13
24.	Parameter Estimation (CH 3)	10. 9. 15
25.	Invited Lecture - Dr. Hongjiang Zhang (Microsoft Advanced Technology Center)	10. 9. 20
26.	Parameter Estimation (CH 3)	10. 09. 27
27.	Parameter Estimation (CH 3)	10. 09. 29
28.	Component analysis and discriminants (CH 3) - PCA/LDA (1)	10. 10. 04
29.	Component analysis and discriminants (CH 3) - PCA/LDA	10. 10. 06
30.	Face Recognition	10. 10. 11
31.	Face Recognition	10. 10. 13