10-708 Probabilistic Graphical Models__CMU__Eric Xing

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2019-07-07 18:08:04
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https://sailinglab.github.io/pgm-spring-2019/ Probabilistic Graphical Models 10-708 • Spring 2019 • Carnegie Mellon University 附有字幕食用
夕惕若厉 行胜于言
视频选集
(24/27)
Lecture 01 - Introduction
01:10:13
Lecture 02 - Representation_ Directed GMs (BNs)
01:18:08
Lecture 03 - Representation_ Undirected GMs (MRFs)
01:15:23
Lecture 04 - Exact Inference_ Variable Elimination
01:22:17
Lecture 06 - Learning partially observed GM
01:02:07
Lecture 07 - Maximum likelihood learning of undirected GM
01:19:21
Lecture 08 - Causal Discovery and Inference
01:18:39
Lecture 09 - Modeling networks_ Gaussian graphical models and Ising models
01:22:28
Lecture 10 - Sequential Models
01:15:55
Lecture 11 - Sequential Models (cont'd) and Approximate Inference
01:23:12
Lecture 12 - Belief Propagation (cont'd) and Theory of Variational Inference
01:20:29
Lecture 13 - Approximate Inference Monte Carlo and Sequential Monte Carlo method
01:24:04
Lecture 14 - Markov Chain Monte Carlo
01:18:42
Lecture 15 - Statistical and Algorithmic Foundations of Deep Learning
01:25:31
Lecture 16 - Building Blocks of DL
57:27
Lecture 17 - Deep Generative Models_Overview and Connections
01:16:56
Lecture 18 - Deep Generative Models (part II)
57:12
Lecture 19 - Case Study_Text Generation
01:12:15
Lecture 20 - Sequential decision making (part 1)_The framework
01:11:03
Lecture 21 - Sequential decision making (part 2)_The algorithms
01:03:49
Lecture 22 - Bayesian Nonparametrics
01:14:51
Lecture 23 - Bayesian Nonparametrics (cont'd)
01:04:55
Lecture 24 - Integrative Paradigms of GMs_Regularized Bayesian Methods
01:15:01
Lecture 25 - Elements of Spectral and Kernel GMs
01:09:51
Lecture 26 - Gaussian Processes and Elements of Metalearning
01:19:21
Lecture 27 - Scalable Algorithms and Systems for Learning, Inference and Predict
01:25:57
Lecture 27-2 - A Civil Engineering Perspective on AI
01:19:42
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