About this Course
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다음 전문 분야의 3개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 24시간 필요

영어

자막: 영어

귀하가 습득할 기술

AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field

다음 전문 분야의 3개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 24시간 필요

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 16분 필요

Learning: Overview

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

...
1 video (Total 16 min)
1개의 동영상
완료하는 데 1시간 필요

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 videos (Total 59 min)
6개의 동영상
Model Selection and Train Validation Test Sets 12m
Diagnosing Bias vs Variance 7m
Regularization and Bias Variance11m
완료하는 데 2시간 필요

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.

...
5 videos (Total 77 min), 2 quizzes
5개의 동영상
Bayesian Prediction13m
Bayesian Estimation for Bayesian Networks17m
2개 연습문제
Learning in Parametric Models18m
Bayesian Priors for BNs8m
2
완료하는 데 21시간 필요

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.

...
3 videos (Total 52 min), 2 quizzes
1개 연습문제
Parameter Estimation in MNs6m
3
완료하는 데 17시간 필요

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.

...
7 videos (Total 106 min), 3 quizzes
7개의 동영상
Bayesian Scores20m
Learning Tree Structured Networks12m
Learning General Graphs: Heuristic Search23m
Learning General Graphs: Search and Decomposability15m
2개 연습문제
Structure Scores10m
Tree Learning and Hill Climbing8m
4
완료하는 데 22시간 필요

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.

...
5 videos (Total 83 min), 3 quizzes
5개의 동영상
EM in Practice11m
Latent Variables22m
2개 연습문제
Learning with Incomplete Data8m
Expectation Maximization14m
4.6
31개의 리뷰Chevron Right

43%

이 강좌를 수료한 후 새로운 경력 시작하기

31%

이 강좌를 통해 확실한 경력상 이점 얻기

18%

급여 인상 또는 승진하기

Probabilistic Graphical Models 3: Learning의 최상위 리뷰

대학: 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.

대학: 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.

강사

Avatar

Daphne Koller

Professor
School of Engineering

스탠퍼드 대학교 정보

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 전문 분야 정보

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

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • 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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