Все публикации

IR20.7 Learning to0:04:24

IR20.7 Learning to rank for Information Retrieval

IR20.11 Summary0:01:03

IR20.11 Summary

IR20.8 Learning to0:02:32

IR20.8 Learning to rank with an SVM

PCA 11: Eigenvector0:11:55

PCA 11: Eigenvector = direction of maximum variance

PCA 20: Linear0:02:59

PCA 20: Linear discriminant analysis

PCA 18: When0:01:52

PCA 18: When principal components fail

PCA 16: Eigenface0:03:57

PCA 16: Eigenface representation

PCA 10: Low-dimensional0:02:14

PCA 10: Low-dimensional projections of data

PCA 7: Why0:03:00

PCA 7: Why we maximize variance in PCA

PCA 9: Finding0:05:21

PCA 9: Finding eigenvalues and eigenvectors

PCA 21: Pros0:01:59

PCA 21: Pros and cons of dimensionality reduction

PCA 13: How0:03:57

PCA 13: How many principal components to use?

PCA 15: Eigen-faces0:05:01

PCA 15: Eigen-faces

PCA 8: Principal0:08:26

PCA 8: Principal components = eigenvectors

PCA 14: Principal0:03:49

PCA 14: Principal component analysis for the impatient

PCA 17: Properties0:02:18

PCA 17: Properties of eigenfaces

PCA 6: Principal0:02:39

PCA 6: Principal component analysis

PCA 19: Classification0:01:48

PCA 19: Classification with PCA features

EM.2: Expectation-maximization algorithm0:04:53

EM.2: Expectation-maximization algorithm

EM.1: Introduction to0:02:33

EM.1: Introduction to mixture models

Clustering 1: Overview0:01:50

Clustering 1: Overview

Clustering 3: Types0:02:54

Clustering 3: Types of clustering algorithms

Clustering 10: Intrinsic0:12:51

Clustering 10: Intrinsic evaluation and alignment

Clustering 6: The0:03:33

Clustering 6: The k-means algorithm visually