MLVU 8.4: Expectation-maximization from first principles

preview_player
Показать описание
In the previous video, we showed how EM works based on informal arguments. Here, we derive it from first principles.

lecturer: Peter Bloem
  1. MLVU
  2. machine learning
  3. lecture
  4. slides
Рекомендации по теме
8 Probability 2: 1:12:56

8 Probability 2: Maximum Likelihood, Gaussian Mixture Models and Expectation Maximization (MLVU2019)

13.4 Expectation-maximization algorithm 0:14:51

13.4 Expectation-maximization algorithm

08 Probabilistic Models 1:25:03

08 Probabilistic Models 2, Normal Distributions, Gaussian Mixtures and EM (MLVU2018)

Expectation and Maximization 0:32:15

Expectation and Maximization

8 Probability 2: 1:32:17

8 Probability 2: Density estimation. Maximum likelihood, Normal Distributions,

CS8850: MLE via 0:38:29

CS8850: MLE via EM

AI660-Dr.Khalid-Expectation Maximization Part 0:39:58

AI660-Dr.Khalid-Expectation Maximization Part 1

Tutorial on EM 1:16:24

Tutorial on EM algorithm – Part 3

K-Means Clustering, Expectation 0:41:58

K-Means Clustering, Expectation Maximization Algorithm

MLVU 9.4: Variational 0:20:13

MLVU 9.4: Variational autoencoders (VAEs)

05 Probabilistic Models 1:32:46

05 Probabilistic Models 1: Naive Bayes, Entropy, Logistic Regression (MLVU2018)

6 Linear Models 1:27:32

6 Linear Models 2: Neural Networks, Backpropagation, SVMs and Kernel methods (MLVU2019)

9 Deep Learning 1:27:21

9 Deep Learning 2: Generative models, GANs, Variational Autoencoders (VAEs) (MLVU2019)

09 Deep Learning 1:33:13

09 Deep Learning 2: GANs, Variational Autoencoders (MLVU2018)

7 Deep learning 1:33:51

7 Deep learning 1: Backpropagation for tensors, Convolutional Neural Networks (MLVU2019)

Lecture 10 Linear 0:27:26

Lecture 10 Linear Classifier II

Lecture 9 - 1:10:50

Lecture 9 - Deep Learning Foundations by Soheil Feizi: Are Adversarial Examples Inevitable?

Lecture #2b: Decision 1:30:00

Lecture #2b: Decision Trees; Over-fitting (9/23/2019)