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Computational and Graphical Models in Probability

Computational and Graphical Models in Probability

本课程是 Statistical Methods for Computer Science 专项课程的一部分

位教师：Ian McCulloh

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Master techniques for simulating random variables, including the Inverse Transformation and Rejection Methods using R programming.
Analyze complex networks using Exponential Random Graph Models to model and interpret social structures and their dependencies.
Understand and apply probabilistic graphical models, including Bayesian networks, to reason about uncertainty and infer relationships in data.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

8 项作业

授课语言：英语（English）

91%

of learners achieved a positive career outcome

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Statistical Methods for Computer Science 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

The course "Computational and Graphical Models in Probability" equips learners with essential skills to analyze complex systems through simulation techniques and network analysis. By exploring advanced concepts such as Exponential Random Graph Models and Probabilistic Graphical Models, students will learn to model and interpret intricate social structures and dependencies within data.

What sets this course apart is its emphasis on practical applications using the R programming language, empowering students to simulate random variables effectively and construct sophisticated models for real-world scenarios. Through hands-on projects and exercises, learners will not only deepen their theoretical understanding but also gain valuable experience in solving applied problems across various domains. Upon completion, you will be well-prepared to tackle challenges in data analysis, machine learning, and statistical modeling, making you a valuable asset in any data-driven field. Whether you're looking to enhance your expertise or start a new career, this course offers a unique blend of theory and practical skills that will enable you to excel in today’s data-centric world.

单元详情

This course covers advanced techniques in network and probabilistic modeling, including simulation methods, exponential random graph models, and probabilistic graphical models. You will gain practical skills in analyzing complex systems and relational data.

涵盖的内容

2篇阅读材料1个插件

This module develops student proficiency in simulating random variables for arbitrary density functions. Students will be introduced to the Inverse Transformation Method and the Rejection Method.

涵盖的内容

4个视频2篇阅读材料3个作业1个非评分实验室

4个视频总计72分钟

The Inverse Transformation Method12分钟
The Rejection Method Part 119分钟
The Rejection Method Part 211分钟
R Tutorial30分钟

2篇阅读材料总计60分钟

Reading References30分钟
Reading References30分钟

3个作业总计90分钟

Random Variable Generation Techniques15分钟
R Tutorial15分钟
Simulation60分钟

1个非评分实验室总计60分钟

Practice Lab: Simulating Random Variables in R60分钟

Exponential Random Graph Models introduce the use of exponential random graph models (ERGMs) for network analysis. You will learn how to model and interpret complex social and relational structures.

涵盖的内容

2个视频2篇阅读材料2个作业1个非评分实验室

2个视频总计71分钟

Exponential Random Graphical Models36分钟
Stochastic Oriented Actor Models35分钟

2篇阅读材料总计60分钟

Reference data20分钟
Reading References40分钟

2个作业总计75分钟

Advanced Network Modeling Techniques15分钟
Exponential Random Graph Models60分钟

1个非评分实验室总计60分钟

Practice Lab: Performing ERGM on Gray’s Anatomy Dataset60分钟

This module introduces a framework for encoding probability distributions over complex joint domains over large numbers of random variables that interact with one another. Students will become familiar with probabilistic graph model applications to many machine learning problems.