Все публикации

0:33:45

12 Inference in Latent Variable Models, pt 3/4 The Variational Auto

0:12:32

12 Inference in Latent Variable Models, pt 4/4 Results and Summary

0:28:15

12 Inference in Latent Variable Models, pt 2/4 Expectation

0:18:59

12 Inference in Latent Variable Models, pt 1/4 Latent Variable Models and Gaussian Mixtures

0:08:18

11 Unsupervised Learning, pt 2/2 Simple Models

0:04:35

11 Unsupervised Learning, pt 1/2 Introduction

0:10:00

10 Gaussian Processes, pt 1/3 Basics

0:26:43

08 Linear Classification, pt 3/3 Generative Models

0:22:37

08 Linear Classification, pt 1/3 Linear Discriminant Functions

0:18:38

08 Linear Classification, pt 2/3 Linear Discriminant Models

0:09:37

07 Kernels, pt 2/4 The Dual Form

0:05:41

07 Kernels, pt 3/4 Kernels Properties

0:06:43

07 Kernels, pt 4/4 An Example & Summary

0:09:55

07 Kernels, pt 1/4 Why Do We Need Kernels

0:15:53

06 Linear Regression, pt 3/3 Online Formulations

0:21:57

06 Linear Regression, pt 1/3 The Basics

0:12:06

06 Linear Regression, pt 2/3 Bayesian Regression

0:21:42

05 Multivariate Normals, pt 3/3 Conditioning Gaussians

0:20:13

05 Multivariate Normals, pt 2/3 Working with Gaussians

0:19:13

05 Multivariate Normals, pt 1/3 Basics

0:07:26

04 Parameter Inference, pt 5/5 Advanced Example and Summary

0:12:58

04 Parameter Inference, pt 4/5 Fully Bayesian Analysis

0:14:59

04 Parameter Inference, pt 3/5 Maximum A Posteriori Estimation

0:10:49

04 Parameter Inference, pt 2/5 The Posterior