Introduction to Pattern Recognition

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.

Introduction to Pattern Recognition (Ch 1)

1.		Introduction to Pattern Recognition (Ch 1)
2.		Statistical Decision Theory (Ch 2)
		Statistical Decision Theory (Ch 2) continued
		Statistical Decision Theory (Ch 2) continued
		Statistical Decision Theory (Ch 2) continued
3.		Parameter Estimation (Ch 3)
		Parameter Estimation (Ch 3) continued
		Parameter Estimation (Ch 3) continued
		Parameter Estimation (Ch 3) continued
4.		Introduction to Principal Component Analysis (PCA)
5.		Introduction to Linear Discriminant Analysis (LDA)
6.		Face Recognition
		Face Recognition continued
7.		Non-parametric Techniques
		Non-parametric Techniques continued
		Non-parametric Techniques continued
		Non-parametric Techniques continued
		Non-parametric Techniques continued
8.		Neural Networks (1)
		Neural Networks (2)
9.		Decision Trees
10.		Classifier Combination (1)
		Classifier Combination (2)
		Classifier Combination (3)
11.		Feature Selection
12.		Unsupervised Learning, Clustering (1)
		Unsupervised Learning, Clustering (2)
		Unsupervised Learning, Clustering (3)