このコースについて
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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。

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上級レベル

英語

字幕:英語

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AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field

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

英語

字幕:英語

シラバス - 本コースの学習内容

1
16分で修了

Learning: Overview

This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.

...
1件のビデオ (合計16分)
1件のビデオ
1時間で修了

Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)

This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.

...
6件のビデオ (合計59分)
6件のビデオ
Model Selection and Train Validation Test Sets 12 分
Diagnosing Bias vs Variance 7 分
Regularization and Bias Variance11 分
2時間で修了

Parameter Estimation in Bayesian Networks

This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.

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5件のビデオ (合計77分), 2 quizzes
5件のビデオ
Bayesian Prediction13 分
Bayesian Estimation for Bayesian Networks17 分
2の練習問題
Learning in Parametric Models18 分
Bayesian Priors for BNs8 分
2
21時間で修了

Learning Undirected Models

In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.

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3件のビデオ (合計52分), 2 quizzes
1の練習問題
Parameter Estimation in MNs6 分
3
17時間で修了

Learning BN Structure

This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.

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7件のビデオ (合計106分), 3 quizzes
7件のビデオ
Bayesian Scores20 分
Learning Tree Structured Networks12 分
Learning General Graphs: Heuristic Search23 分
Learning General Graphs: Search and Decomposability15 分
2の練習問題
Structure Scores10 分
Tree Learning and Hill Climbing8 分
4
22時間で修了

Learning BNs with Incomplete Data

In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.

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5件のビデオ (合計83分), 3 quizzes
5件のビデオ
EM in Practice11 分
Latent Variables22 分
2の練習問題
Learning with Incomplete Data8 分
Expectation Maximization14 分
4.6
31件のレビューChevron Right

43%

コース終了後に新しいキャリアをスタートした

31%

コースが具体的なキャリアアップにつながった

18%

昇給や昇進につながった

Probabilistic Graphical Models 3: Learning からの人気レビュー

by LLJan 30th 2018

very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.

by ZZFeb 14th 2017

Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.

講師

Avatar

Daphne Koller

Professor
School of Engineering

スタンフォード大学(Stanford University)について

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States....

Probabilistic Graphical Models の専門講座について

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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  • Compute the sufficient statistics of a data set that are necessary for learning a PGM from data

    Implement both maximum likelihood and Bayesian parameter estimation for Bayesian networks

    Implement maximum likelihood and MAP parameter estimation for Markov networks

    Formulate a structure learning problem as a combinatorial optimization task over a space of network structure, and evaluate which scoring function is appropriate for a given situation

    Utilize PGM inference algorithms in ways that support more effective parameter estimation for PGMs

    Implement the Expectation Maximization (EM) algorithm for Bayesian networks

    Honors track learners will get hands-on experience in implementing both EM and structure learning for tree-structured networks, and apply them to real-world tasks

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