Uncertainty Modeling in AI | Lecture 10 (Part 2): Variational inference

preview_player
Показать описание
Here's the video lectures of CS5340 - Uncertainty Modeling in AI (Probabilistic Graphical Modeling) taught at the Department of Computer Science, National University of Singapore (NUS).

This is an introductory course on probabilistic graphical modeling which was recorded for online learning at NUS due to COVID-19. The topics covered include:

Lecture 1: Introduction to probabilistic reasoning
Lecture 2: Bayesian networks (Directed graphical models)
Lecture 3: Markov random fields (Undirected graphical models)
Lecture 4: Variable elimination and belief propagation
Lecture 5: Factor graph and the junction tree algorithm
Lecture 6: Parameter learning with complete data
Lecture 7: Mixture models and the EM algorithm
Lecture 8: Hidden Markov Models (HMM)
Lecture 9: Monte Carlo inference (Sampling)
Lecture 10: Variational inference
Lecture 11: Variational Auto Encoder and Mixture Density Networks
Lecture 12: Graph cut and alpha expansion

Disclaimer: This video lecture is provided freely for your reference. The lecturer and NUS are not responsible for anything expressed herein.
  1. CVRP Lab at NUS
Рекомендации по теме
Uncertainty (Aleatoric vs 0:10:18

Uncertainty (Aleatoric vs Epistemic) | Machine Learning

Uncertainty Modeling in 0:32:05

Uncertainty Modeling in AI | Lecture 1 (Part 3): Introduction to Probabilistic Reasoning

Uncertainty Modeling in 0:28:03

Uncertainty Modeling in AI | Lecture 1 (Part 1): Introduction to Probabilistic Reasoning

Uncertainty Modeling in 1:16:14

Uncertainty Modeling in AI | Lecture 2 (Part 1): Bayesian networks (Directed graphical models)

Uncertainty Modeling in 0:57:15

Uncertainty Modeling in AI | Lecture 6 (Part 1): Parameter learning with complete data

Uncertainty - Lecture 1:54:30

Uncertainty - Lecture 2 - CS50's Introduction to Artificial Intelligence with Python 2020

Why Your AI 0:01:11

Why Your AI is Uncertain (Advanced Modeling in AI Uncertainty)

Uncertainty Modeling in 0:53:57

Uncertainty Modeling in AI | Lecture 10 (Part 1): Variational inference

Uncertainty Modeling in 0:39:10

Uncertainty Modeling in AI | Lecture 2 (Part 2): Bayesian networks (Directed graphical models)

Quantifying the Uncertainty 0:33:29

Quantifying the Uncertainty in Model Predictions

Uncertainty Modeling in 0:51:41

Uncertainty Modeling in AI | Lecture 8 (Part 1): Hidden Markov Models (HMM)

Uncertainty Modeling in 0:44:49

Uncertainty Modeling in AI | Lecture 7 (Part 1): Mixture models and the EM algorithm

Solving AI Uncertainty 0:37:49

Solving AI Uncertainty with NYU Prof. Andrew Wilson

Uncertainty Modeling in 0:38:11

Uncertainty Modeling in AI | Lecture 5 (Part 1): Factor graph and the junction tree algorithm

Uncertainty Modeling in 0:49:19

Uncertainty Modeling in AI | Lecture 9 (Part 1): Monte Carlo inference (Sampling)

Uncertainty Modeling in 0:51:58

Uncertainty Modeling in AI | Lecture 7 (Part 2): Mixture models and the EM algorithm

Uncertainty Modeling in 0:37:06

Uncertainty Modeling in AI | Lecture 6 (Part 2): Parameter learning with complete data

Uncertainty Modeling in 0:40:36

Uncertainty Modeling in AI | Lecture 9 (Part 3): Monte Carlo inference (Sampling)

Uncertainty Modeling in 0:40:22

Uncertainty Modeling in AI | Lecture 6 (Part 3): Parameter learning with complete data

[Artificial Intelligence] Show 0:02:01

[Artificial Intelligence] Show or Suppress? Managing Uncertainty in Model Explanations

Uncertainty Modeling in 0:42:33

Uncertainty Modeling in AI | Lecture 1 (Part 2): Introduction to Probabilistic Reasoning

Uncertainty Modeling in 0:14:24

Uncertainty Modeling in AI | Lecture 11 (Part 3): VAE and Mixture Density Networks

Uncertainty Modeling in 1:25:50

Uncertainty Modeling in AI | Lecture 12 (Part 1): Graph cut and alpha expansion

Uncertainty Modeling in 0:58:05

Uncertainty Modeling in AI | Lecture 10 (Part 2): Variational inference