응용수학 1

고려대학교
박주영

페이스북 트위터 카카오톡 네이버밴드 링크복사

주제분류

공학 >전기ㆍ전자 >제어계측공학
강의학기

2015년 1학기

조회수

13,534
평점

3/5.0 (3)

강의계획서

기계학습(machine learning)에 사용되는 각종 응용수학관련 주제를 다룸

수강안내 및 수강신청
※ 수강확인증 발급을 위해서는 수강신청이 필요합니다

Probability distributions (ch.2)

차시별 강의

1.	Probability distributions (ch.2)
	Probability distributions (ch.2)	Binary variables; Multinomial variables; Gaussian distribution
	Probability distributions (ch.2)	Location-dispersion ellipsoid; Properties of Gaussians
2.	Probability distributions (ch.2); Linear models for regression (ch.3)	Exponential family; Gradient-descent method; Least-squares method
	Probability distributions (ch.2); Linear models for regression (ch.3)
3.	Linear models for regression (ch.3)	Maximum likelihood solution
	Linear models for regression (ch.3)
4.	Linear models for regression (ch.3)	Bayesian linear regression; Predictive distribution
	Linear models for regression (ch.3)
5.	Linear models for classification (ch.4)	Linear discriminant analysis
	Linear models for classification (ch.4)
6.	Linear models for classification (ch.4); Neural networks (ch.5)	Logistic regression; Feedforward neural networks
	Linear models for classification (ch.4); Neural networks (ch.5)
7.	Kernel methods (ch.6); Sparse kernel machines (ch.7)	Gaussian process regression; SVM for binary classification
	Kernel methods (ch.6); Sparse kernel machines (ch.7)
8.	Sparse kernel machines (ch.7)	Convex optimization; KKT conditions; Dual problem
	Sparse kernel machines (ch.7)
9.	Sparse kernel machines (ch.7)	Overlapping class distributions; SVM for regression
	Sparse kernel machines (ch.7)
10.	Sparse kernel machines (ch.7)	SVDD; One-class nu-SVM
	Sparse kernel machines (ch.7)
11.	Sparse kernel machines (ch.7); Continuous latent variables (ch.12)	Nu-SVM for binary classification; Principal component analysis; Kernel principal component analysis
	Sparse kernel machines (ch.7); Continuous latent variables (ch.12)
12.	Continuous latent variables (ch.12)	ML PCA, EM algorithm for PPCA
	Continuous latent variables (ch.12)