• 国家新闻出版署、全国“扫黄打非”办公室联合部署2018年网络文学专项整治行动 2019-06-25
  • 候选案例:关于为明天·一起善行 2019-06-18
  • 山西:“一县一策”集中攻坚深度贫困县——黄河新闻网 2019-06-18
  • 国台办谈落实“31条惠台措施”取得新进展:“措施”在沪落地  有上海特色 2019-06-07
  • 首批战机未抵,英工程师就"向中国泄露F 2019-05-24
  • 【理上网来·喜迎十九大】以全面从严治党调动党员干部积极性主动性创造性 2019-05-20
  • 改革开放四十年泸州老窖:中国荣耀桂冠之重泸州 老窖 2019-05-17
  • 新车图解:e5 450 续航提升至480公里 2019-05-04
  • 人民群众是我们力量的源泉 2019-05-04
  • 新华社评论员:聚焦新目标 开启新征程 2019-04-14
  • 德州齐河司法所开展人民调解“回头看”工作 2019-04-14
  • 辽宁:电商成为精准扶贫的“利器” 2019-03-28
  • 人民网评:教师欠薪为何又成新闻了? 2019-03-23
  • 张继科状态低迷 刘国梁倍感压力 2019-03-23
  • 湖北浠水十月村经济史料及其研究价值 2019-03-17
  • 2018今日七星彩开奖:概率图形模型专项课程

    Probabilistic Graphical Models

    体彩排列3和值走势图 www.3l5g.net Master a new way of reasoning and learning in complex domains

    斯坦福大学

    Coursera

    计算机

    难(高级)

    4 个月

    • 英语
    • 1687

    课程概况

    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.

    你将学到什么

    Inference

    Bayesian Network

    Belief Propagation

    Graphical Model

    包含课程

    课程1
    Probabilistic Graphical Models 1: Representation

    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.

    课程2
    Probabilistic Graphical Models 2: Inference

    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.

    课程3
    Probabilistic Graphical Models 3: Learning

    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.

    HEC Managing Innovation & Design Thinking – Join Today And Inspire Innovation
    声明:MOOC中国发布之课程均源自下列机构,版权均归他们所有。本站仅作报道收录并尊重其著作权益,感谢他们对MOOC事业做出的贡献!(排名不分先后)
    • Coursera
    • edX
    • OpenLearning
    • FutureLearn
    • iversity
    • Udacity
    • NovoEd
    • Canvas
    • Open2Study
    • Google
    • ewant
    • FUN
    • IOC-Athlete-MOOC
    • World-Science-U
    • Codecademy
    • CourseSites
    • opencourseworld
    • ShareCourse
    • gacco
    • MiriadaX
    • JANUX
    • openhpi
    • Stanford-Open-Edx
    • 网易云课堂
    • 中国大学MOOC
    • 学堂在线
    • 顶你学堂
    • 华文慕课
    • 好大学在线CnMooc
    • 以及更多...

    © 2008-2018 www.3l5g.net 慕课改变你,你改变世界

  • 国家新闻出版署、全国“扫黄打非”办公室联合部署2018年网络文学专项整治行动 2019-06-25
  • 候选案例:关于为明天·一起善行 2019-06-18
  • 山西:“一县一策”集中攻坚深度贫困县——黄河新闻网 2019-06-18
  • 国台办谈落实“31条惠台措施”取得新进展:“措施”在沪落地  有上海特色 2019-06-07
  • 首批战机未抵,英工程师就"向中国泄露F 2019-05-24
  • 【理上网来·喜迎十九大】以全面从严治党调动党员干部积极性主动性创造性 2019-05-20
  • 改革开放四十年泸州老窖:中国荣耀桂冠之重泸州 老窖 2019-05-17
  • 新车图解:e5 450 续航提升至480公里 2019-05-04
  • 人民群众是我们力量的源泉 2019-05-04
  • 新华社评论员:聚焦新目标 开启新征程 2019-04-14
  • 德州齐河司法所开展人民调解“回头看”工作 2019-04-14
  • 辽宁:电商成为精准扶贫的“利器” 2019-03-28
  • 人民网评:教师欠薪为何又成新闻了? 2019-03-23
  • 张继科状态低迷 刘国梁倍感压力 2019-03-23
  • 湖北浠水十月村经济史料及其研究价值 2019-03-17
  • 水果拉霸怎样可以爆分 黑龙江11选5最大遗漏 刀塔自走棋信使图鉴 阿里巴巴矢量图 第五人格游戏下载 山东11选5任选4 apex英雄手游下载 12.31森林狼vs小牛 热血传奇客户端官网下载 酷搜马戏团走势图 新版的内蒙古十一选五开奖结果 现在球球大作战用户名空白名 天天酷跑新坐骑圣龙 河北十一选五走势图 阿尔希拉尔郭泰辉 幸运飞艇10计划7码