このコースについて

21,616 最近の表示

受講生の就業成果

50%

コース終了後に新しいキャリアをスタートした

20%

コースが具体的なキャリアアップにつながった

20%

昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
上級レベル
約36時間で修了
英語
字幕:英語

習得するスキル

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation

受講生の就業成果

50%

コース終了後に新しいキャリアをスタートした

20%

コースが具体的なキャリアアップにつながった

20%

昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
上級レベル
約36時間で修了
英語
字幕:英語

提供:

スタンフォード大学(Stanford University) ロゴ

スタンフォード大学(Stanford University)

シラバス - 本コースの学習内容

1

1

25分で修了

Inference Overview

25分で修了
2件のビデオ (合計25分)
2件のビデオ
Overview: MAP Inference9 分
1時間で修了

Variable Elimination

1時間で修了
4件のビデオ (合計56分)
4件のビデオ
Complexity of Variable Elimination12 分
Graph-Based Perspective on Variable Elimination15 分
Finding Elimination Orderings11 分
1の練習問題
Variable Elimination18 分
2

2

18時間で修了

Belief Propagation Algorithms

18時間で修了
9件のビデオ (合計150分)
9件のビデオ
Properties of Cluster Graphs15 分
Properties of Belief Propagation9 分
Clique Tree Algorithm - Correctness18 分
Clique Tree Algorithm - Computation16 分
Clique Trees and Independence15 分
Clique Trees and VE16 分
BP In Practice15 分
Loopy BP and Message Decoding21 分
2の練習問題
Message Passing in Cluster Graphs10 分
Clique Tree Algorithm10 分
3

3

1時間で修了

MAP Algorithms

1時間で修了
5件のビデオ (合計74分)
5件のビデオ
Finding a MAP Assignment3 分
Tractable MAP Problems15 分
Dual Decomposition - Intuition17 分
Dual Decomposition - Algorithm16 分
1の練習問題
MAP Message Passing4 分
4

4

14時間で修了

Sampling Methods

14時間で修了
5件のビデオ (合計100分)
5件のビデオ
Markov Chain Monte Carlo14 分
Using a Markov Chain15 分
Gibbs Sampling19 分
Metropolis Hastings Algorithm27 分
2の練習問題
Sampling Methods14 分
Sampling Methods PA Quiz8 分
26分で修了

Inference in Temporal Models

26分で修了
1件のビデオ (合計20分)
1件のビデオ
1の練習問題
Inference in Temporal Models6 分

レビュー

PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE からの人気レビュー

すべてのレビューを見る

Probabilistic Graphical Models 専門講座について

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems....
Probabilistic Graphical Models

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  • Execute the basic steps of a variable elimination or message passing algorithm

    Understand how properties of the graph structure influence the complexity of exact inference, and thereby estimate whether exact inference is likely to be feasible

    Go through the basic steps of an MCMC algorithm, both Gibbs sampling and Metropolis Hastings

    Understand how properties of the PGM influence the efficacy of sampling methods, and thereby estimate whether MCMC algorithms are likely to be effective

    Design Metropolis Hastings proposal distributions that are more likely to give good results

    Compute a MAP assignment by exact inference

    Honors track learners will be able to implement message passing algorithms and MCMC algorithms, and apply them to a real world problem

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