About this 전문분야
15,686

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 일정

유연한 마감을 설정하고 유지 관리합니다.

고급 단계

완료하는 데 약 4개월 필요

매주 6시간 권장

영어

자막: 영어

귀하가 습득할 기술

InferenceBayesian NetworkBelief PropagationGraphical Model

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 일정

유연한 마감을 설정하고 유지 관리합니다.

고급 단계

완료하는 데 약 4개월 필요

매주 6시간 권장

영어

자막: 영어

How the 전문분야 Works

강좌 수강

Coursera 전문 분야는 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 전문 분야에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 하나의 전문 분야에 속하는 강좌에 등록하면 해당 전문 분야 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료해도 됩니다. — 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.

실습 프로젝트

모든 전문 분야에는 실습 프로젝트가 포함되어 있습니다. 전문 분야를 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 전문 분야에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우 각 강좌를 완료해야 프로젝트를 시작할 수 있습니다.

수료증 취득

모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.

how it works

이 전문분야에는 3개의 강좌가 있습니다.

강좌1

Probabilistic Graphical Models 1: Representation

4.7
(1,021개의 평가)
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. This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly....
강좌2

Probabilistic Graphical Models 2: Inference

4.6
(340개의 평가)
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. This course is the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem....
강좌3

Probabilistic Graphical Models 3: Learning

4.6
(205개의 평가)
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. This course is the third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem....

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....

자주 묻는 질문

  • 네! 시작하려면 관심 있는 강좌 카드를 클릭하여 등록합니다. 강좌를 등록하고 완료하면 공유할 수 있는 인증서를 얻거나 강좌를 청강하여 강좌 자료를 무료로 볼 수 있습니다. 전문 분야 과정에 있는 강좌에 등록하면, 전체 전문 분야에 등록하게 됩니다. 학습자 대시보드에서 진행 사항을 추적할 수 있습니다.

  • 이 강좌는 100% 온라인으로 진행되므로 강의실에 직접 참석할 필요가 없습니다. 웹 또는 모바일 장치를 통해 언제 어디서든 강의, 읽기 자료, 과제에 접근할 수 있습니다.

  • The Specialization has three five-week courses, for a total of fifteen weeks.

  • This class does require some abstract thinking and mathematical skills. However, it is designed to require fairly little background, and a motivated student can pick up the background material as the concepts are introduced. We hope that, using our new learning platform, it should be possible for everyone to understand all of the core material.

    Though, you should be able to program in at least one programming language and have a computer (Windows, Mac or Linux) with internet access (programming assignments will be conducted in Matlab or Octave). It also helps to have some previous exposure to basic concepts in discrete probability theory (independence, conditional independence, and Bayes' rule).

  • For best results, the courses should be taken in order.

  • No.

  • You will be able to take a complex task and understand how it can be encoded as a probabilistic graphical model. You will now know how to implement the core probabilistic inference techniques, how to select the right inference method for the task, and how to use inference to reason. You will also know how to take a data set and use it to learn a model, whether from scratch, or to refine or complete a partially specified model.

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