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所在平台: Udemy |
课程主页: https://www.udemy.com/course/bayesian-statistics/
课程评论：没有评论
课程名称：贝叶斯统计 课程概述：贝叶斯统计是一个引人入胜的领域，如今已成为数据科学和机器学习中许多统计应用的核心。在本课程中，我们将覆盖贝叶斯统计的主要概念，其中包括贝叶斯定理、贝叶斯网络、网络推理中的枚举和消除方法、采样方法（如吉布斯采样和梅特罗波利斯-黑斯廷斯算法）、贝叶斯推断以及其与机器学习的关系。课程围绕实例和练习设计，为学员提供了大量的机会以建立直觉并应用所学知识。许多示例来自科学、商业或工程的实际应用，或源自数据科学面试中的问题。虽然这不是一门编程课程，但我也提供了多个与贝叶斯统计相关的编程资源参考。该课程特别为没有多年 formal 数学教育的学生设计，唯一的先决条件是高中水平的数学，理想情况下为大一的数学课程以及对概率的基本理解。
Bayesian Statistics is a fascinating field and today the centerpiece of many statistical applications in data science and machine learning. In this course, we will cover the main concepts of Bayesian Statistics including among others Bayes Theorem, Bayesian networks, Enumeration & Elimination for inference in such networks, sampling methods such as Gibbs sampling and the Metropolis-Hastings algorithm, Bayesian inference and the relation to machine learning.This course is designed around examples and exercises that provide plenty of opportunities to build intuition and apply your gathered knowledge. Many examples come from real-world applications in science, business or engineering or are taken from data science job interviews.While this is not a programming course, I have included multiple references to programming resources relevant to Bayesian statistics. The course is specifically designed for students without many years of formal mathematical education. The only prerequisite is high-school level mathematics, ideally a first-year university mathematics course and a basic understanding of probability.
