The course "Advanced Probability and Statistical Methods" provides a deep dive into advanced probability and statistical methods, essential for mastering data analysis in computer science. Covering joint distributions, expectation, statistical testing, and Markov chains, you'll explore key concepts and techniques that underpin modern data-driven decision-making. By engaging with real-world problems, you’ll learn to apply these methods effectively, gaining insights into the relationships between random variables and their applications in diverse fields.
Advanced Probability and Statistical Methods
Advanced Probability and Statistical Methods
位教师：Ian McCulloh
访问权限由 New York State Department of Labor 提供
您将学到什么
Learn to analyze relationships between random variables through joint probability distributions and independence concepts.
Understand how to calculate and interpret expected values, variances, and correlations for random variables.
Acquire essential skills in conducting statistical tests, including T-tests and confidence intervals, for data analysis.
Explore the principles of Markov chains and their applications in modeling systems with memoryless properties and calculating entropy.
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作业
22 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
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6个模块
This course provides a comprehensive overview of probability theory and statistical inference, covering joint probability distributions, independence, and conditional distributions. Students will explore expected values, variances, and key statistical theorems, including the central limit theorem. Hypothesis testing, regression analysis, and stochastic processes such as Poisson processes and Markov chains will also be examined. Through practical applications and problem-solving, participants will gain essential skills in data analysis and interpretation.
涵盖的内容
2篇阅读材料1个插件
This module presents the joint distributions of multiple random variables, both discrete and continuous and introduces the concept of independence.
涵盖的内容
9个视频4篇阅读材料5个作业1个非评分实验室
This module focuses on the expectation of a random variable and joint random variable. Students will solve problems using the linearity of expectation and identify when its application is inappropriate. We will also explore variance, covariance, and correlation.
涵盖的内容
7个视频3篇阅读材料4个作业1个非评分实验室
This module will apply several limit theorems to solve problems to include the central limit theorem, the Markov inequality, and the Chebyshev inequality. We will also prove Murphy’s Law.
涵盖的内容
9个视频4篇阅读材料5个作业1个非评分实验室
This module develops student proficiency in probabilistic models to include Markov chains. Students will be introduced to problems involving surprise, uncertainty, and entropy.
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
This module develops student proficiency in probabilistic models to include Markov chains. Students will be introduced to problems involving surprise, uncertainty, and entropy.
涵盖的内容
8个视频4篇阅读材料5个作业1个非评分实验室
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