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所在平台: Udemy |
课程主页: https://www.udemy.com/course/probability-m/
课程评论：没有评论
## 课程总结：机器学习概率基础 本课程由拥有数学双硕士学位的 Suman Mathews 教授，旨在帮助学习者掌握机器学习所需的概率论基础知识。课程以生动的故事开篇，通过一个名为安娜的数学家创建的概率课程，将复杂的概念变得易于理解。 **核心内容包括：** * **概率基础：** 教授概率的基本定义，以及如何应用 `概率 = (有利事件数) / (总事件数)` 的公式解决问题。 * **条件概率与独立事件：** 深入讲解了条件概率、互斥事件和独立事件的概念及其相关性质。 * **全概率与贝叶斯定理：** 阐释了全概率的意义及其如何引出至关重要的贝叶斯定理，并提供大量例题帮助练习。 * **随机变量：** 学习随机变量的概念，区分离散型和连续型随机变量，并学习计算它们的期望值和方差。 * **二项分布：** 讲解二项分布及其与随机变量的区别，以及如何计算二项分布的期望值和方差。 * **泊松分布（附加内容）：** 介绍泊松分布，包括其期望值、方差及在实际问题中的应用。 本课程强调通过多种方法解决问题，使学习者不仅能理解概率，更能将其视为理解世界的有力工具，对于备考 SAT 和 GRE 数学部分也极具帮助。
Once upon a time in the city of Athens, there was a young mathematician named Anna. Anna was always fascinated by the chance of uncertainty and she knew that Probability was the only way to understand this. Wanting to share her knowledge with the world, she created a Probability Course.This Course was open to all and it promised to make Probability easy to understand. As the students gathered, Anna began her class by rolling a dice. She asked her students the probability of getting 7 by rolling two dice. Under Anna's guidance , the students learnt that there were multiple ways to get 7 by rolling two dice. As the course progressed, Anna introduced her students to conditional probability, independent events, Bayes Theorem and more. One of Anna's favourite parts of the course was when the students experimented with random events and realised how to distinguish between a random and Binomial variable.By the end of the course, Anna had inspired her students to understand Probability, not as something to be feared, but to help them understand the world around them. And so, Probability, became a beloved course where students of all backgrounds came to learn, thanks to a mathematician named Anna.Have you wanted to learn Probability on your own? Each problem here follows a different approach. Here's helping you to understanding Probability better.I am Suman Mathews. I have a double master's in Mathematics and I have taught Maths to high school and college students for the past three decades. A good knowledge of Probability will also go a long way in helping you in SAT and GRE QUANT exams.To start with, you'll learn the basic definition of Probability and how to apply it in problem solving. You need to keep in mind that probability=(number of favourable events)/total number of events. You'll learn about Conditional Probability, Mutually exclusive and independent events and properties regarding these.Moving on, you learn about Total Probability and how it leads to Bayes' Theorem. There area a number of solved examples illustrated here for you to practice. This comes to the end of the first part of the course.Next, you'll learn what are Random variables and how to calculate the mean and variance of discrete and continuous random variables. The course teaches you how to distinguish between a discrete and continuous variable. Moving on, you'll come to Binomial distributions and how to check if a variable is a random variable or follows Binomial distribution.This is extremely important and the course teaches you how to distinguish between the two. Learn how to calculate the mean and variance of random variables and a Binomial distribution. You're also given all the formulas taught in the course as one module.Bonus-Learn about the Poisson Distribution in Probability. You'll learn about the mean and variance of the Poisson distribution and its application in problems. Enrol for the course and enhance your learning. Thank you!
