Probability - The Science of Uncertainty and Data

Probability - The Science of Uncertainty and Data课程简介：前往报名学习

Probability - The Science of Uncertainty and Data课程简介：

Build foundational knowledge of data science with this introduction to probabilistic models.

前往报名学习

Probability - The Science of Uncertainty and Data课程目录：

Unit 0: Overview

--Lec. 0: Course overview

--Course introduction, objectives, and study guide

--Syllabus, calendar, and grading policy

--edX Tutorial

--Discussion forum and collaboration guidelines

--Homework mechanics and standard notation

--Textbook information

Unit 1: Probability models and axioms

--Lec. 1: Probability models and axioms

--Mathematical background

--Solved problems

--Unit 1 Discussion forums

Unit 2: Conditioning and independence

--Unit overview

--Lec. 2: Conditioning and Bayes' rule

--Lec. 3: Independence

-- Solved problems

--Problem Set 2

Unit 3: Counting

--Lec. 4: Counting

--Solved problems

--Problem Set 3

Unit 4: Discrete random variables

-- Unit overview

--Lec. 5: Probability mass functions and expectations

-- Lec. 6: Variance; Conditioning on an event; Multiple r.v.'s

--Lec. 7: Conditioning on a random variable; Independence of r.v.'s

--Solved problems

--Additional theoretical material

--Problem Set 4

-- Unit summary

Unit 5: Continuous random variables

--Unit overview

-- Lec. 8: Probability density functions

-- Lec. 9: Conditioning on an event; Multiple r.v.'s

-- Lec. 10: Conditioning on a random variable; Independence; Bayes' rule

--Standard normal table

--Solved problems

-- Problem Set 5

--Unit summary

Unit 6: Further topics on random variables

--Unit overview

--Lec. 11: Derived distributions

-- Lec. 12: Sums of independent r.v.'s; Covariance and correlation

-- Lec. 13: Conditional expectation and variance revisited; Sum of a ra

-- Solved problems

-- Additional theoretical material

--Problem Set 6

--Unit summary

Unit 7: Bayesian inference

-- Unit overview

--Lec. 14: Introduction to Bayesian inference

--Lec. 15: Linear models with normal noise

-- Problem Set 7a

--Lec. 16: Least mean squares (LMS) estimation

--Lec. 17: Linear least mean squares (LLMS) estimation

--Problem Set 7b

--Solved problems

--Additional theoretical material

--Unit summary

Unit 8: Limit theorems and classical statistics

--Unit overview

--Lec. 18: Inequalities, convergence, and the Weak Law of Large Numbers

--Lec. 19: The Central Limit Theorem (CLT)

--Lec. 20: An introduction to classical statistics

--Solved problems

--Additional theoretical material

--Problem Set 8

--Unit summary

Unit 9: Bernoulli and Poisson processes

--Unit overview

-- Lec. 21: The Bernoulli process

-- Lec. 22: The Poisson process

-- Lec. 23: More on the Poisson process

--Solved problems

--Additional theoretical material

--Problem Set 9

--Unit summary

Unit 10: Markov chains

--Unit overview

--Lec. 24: Finite-state Markov chains

--Lec. 25: Steady-state behavior of Markov chains

--Lec. 26: Absorption probabilities and expected time to absorption

--Solved problems

--Problem Set 10

Probability - The Science of Uncertainty and Data授课教师：

Patrick Jaillet-Professor-麻省理工学院-

Patrick Jaillet教授是MIT电子工程与计算机科学系教授，同时是MIT运算研究中心主任，他在MIT取得博士学位，他的研究领域包括最优化方法和在不确定性条件下的决策，可应用于交通和互联网经济等领域，Jaillet教授讲授的科目有算法、最优化方法以及概率论

Dimitri Bertsekas-Professor-麻省理工学院-

Dimitri Bertsekas教授是MIT电子工程与计算机科学系教授和美国国家工程院院士，他在MIT取得博士学位并从1979年开始任教，他的研究专注于最优化方法和算法，侧重于随机系统的研究并应用于数据网络、交通和能源系统等领域，他从事概率论的教学工作已经超过15年。

Qing He----麻省理工学院-

Qing He MIT电子工程与计算机科学系研究生。她的研究兴趣包括统计推断、信号处理和无线通信——所有这些都依赖于在6.041课程中所学的基本概念，她在MIT选修了一些概率论相关课程并在两个学期内担任6.041课程助教。

John Tsitsiklis-Professor-麻省理工学院-

John Tsitsiklis教授是MIT电子工程与计算机科学系教授，是美国国家工程院院士，他在MIT取得博士学位并从1984年开始任教，他的研究专注于随机系统的分析和控制并应用于各种领域，从计算机网络到金融领域，他从事概率论的教学工作已经超过15年。