- 来自www.xuetangx.com
概率论——不确定性的科学
课程
zh
中文
此内容评级为 0/5
- 等级 介绍
课程详情
教学大纲
- 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
- Entrance Survey
- Important Preliminary Survey
- Unit 1: Probability models and axioms
- Lec. 1: Probability models and axioms
- Mathematical background
Sets; sequences, limits, and series; (un)countable sets. - Solved problems
- Problem Set 1
- 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
- Exam 1
- Exam 1
- 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 random number of independent r.v.'s
- 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
- Exam 2
- Exam 2
- 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
- Exit Survey
- Important Exit Survey
- Final Exam
- Final Exam
先决条件
没有。
讲师
- John Tsitsiklis
平台
慕华（北京）网络技术有限公司旗下的学堂在线是免费公开的MOOC（大规模开放在线课程）平台，是教育部在线教育研究中心的研究交流和成果应用平台，致力于通过来自国内外一流名校开设的免费网络学习课程，为公众提供系统的高等教育，让每一个中国人都有机会享受优质教育资源。通过和清华大学在线教育研究中心、以及国内外知名大学的紧密合作，学堂在线将不断增加课程的种类和丰富程度。
此内容评级为 4.5/5
(没有评论)此内容评级为 4.5/5
(没有评论)完成这个资源，写一篇评论