機率
Probability
| 節 | 週二 | 週五 |
|---|---|---|
2 09:00–09:50 | 機率 ED203（光復） | |
5 13:20–14:10 | 機率 ED203（光復） 2 節連堂 | |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
The goal of this course is to teach the fundamental theories, results, and applications of probability. Major topics in this course include but are not limited to discrete and continuous random variables, expectation and moments, functions of multiple random variables, covariance and correlation, conditional probability and expectation, Transforms of random variables, limit theorems, and a brief introduction to discrete time Markov chains. Applications and examples of probability theory will include but are not limited to wireless systems and networks and machine learning.
Calculus. Some understanding of linear algebra is recommended.
無備註
Course materials will be provided on New e3 system. During the pandemic, we are changing to hybrid lecture. Please use the following Google link to join online if you cannot physically attend the class due to COVID19 : Tuesday: 周二機率課程:https://meet.google.com/saw-gerc-njr Friday: 週五機率課程:https://meet.google.com/pav-dsow-wic
Homework (will include 8~12 problem sets): 30% Midterm Exam: 30% Final Exam: 35% In-class participation and in-class performance: 5%
Sample Space and Probability
Chapter 1
- 講授：
- 6
Discrete Random Variables
Chapter 2
- 講授：
- 9
Continuous and General Random Variables
Chapter 3
- 講授：
- 12
Further Topics on Random Variables
Chapter 4
- 講授：
- 9
Limit Theorems
Chapter 5
- 講授：
- 9
An Introduction to Discrete-Time Markov Chains
7.1 Discrete-Time Markov Chains 7.2 Classification of States 7.3 Steady-state behavior
- 講授：
- 6
Selected topics
Poisson processes, Bayesian statistical inference, fundamental of queuing theory, etc.
- 講授：
- 3
| 週次 | 主題 |
|---|---|
| 第 1 週 | Chapter 1: sample space and probability, conditional probability and independence 2023-02-14(二),2023-02-17(五) |
| 第 2 週 | Chapter 2: discrete random variables, probability mass function 2023-02-21(二),2023-02-24(五) |
| 第 3 週 | Chapter 2: functions of random variables, expectation and variance 2023-02-28(二),2023-03-03(五) |
| 第 4 週 | Chapter 2: joint PMF, conditioning independence 2023-03-07(二),2023-03-10(五) |
| 第 5 週 | Chapter 3: continuous random variables, cumulative distribution functions 2023-03-14(二),2023-03-17(五) |
| 第 6 週 | Chapter 3: Normal random variables 2023-03-21(二),2023-03-24(五) |
| 第 7 週 | Chapter 3: joint PDFs of multiple random variables 2023-03-28(二),2023-03-31(五) |
| 第 8 週 | Chapter 3: conditioning for continuous random variables, The continuous Bayes' rules 2023-04-04(二),2023-04-07(五) |
| 第 9 週 | Midterm exam 2023-04-11(二),2023-04-14(五) |
| 第 10 週 | Chapter 4: Derived distributions, covariance and correlation, conditional expectation and variance as random variables 2023-04-18(二),2023-04-21(五) |
| 第 11 週 | Chapter 4: Transforms, sum of a random number of independent random variables 2023-04-25(二),2023-04-28(五) |
| 第 12 週 | Chapter 5: Markov and Chebyshev inequalities, The weak law of large numbers 2023-05-02(二),2023-05-05(五) |
| 第 13 週 | Chapter 5: Convergence in probability, central limit theorem 2023-05-09(二),2023-05-12(五) |
| 第 14 週 | Chapter 5: The strong law of large numbers 2023-05-16(二),2023-05-19(五) |
| 第 15 週 | Chapter 7: Concept of stochastic processes, discrete-time Markov chains 2023-05-23(二),2023-05-26(五) |
| 第 16 週 | Chapter 7: Steady-state behavior of Markov chains 2023-05-30(二),2023-06-02(五) |
| 第 17 週 | Selected topics: Poisson process, Bayesian statistical inference, fundamental of queuing theory, etc. 2023-06-06(二),2023-06-09(五) |
| 第 18 週 | Final exam 2023-06-13(二),2023-06-16(五) |
Introduction to Probability, 2nd Edition, D. P. Bertsekas and J. N. Ysitsiklis, Athena Scientific, 2008.
- 地點
- ED 833.
- 時間
- Friday 10:00~12:00
- 聯絡方式
- Email: mingchunlee@nycu.edu.tw 助教: 1. 黃新評 (benhsp0624@gmail.com ) 2. 蔡岳修 (is3061omyid.ee11@nycu.edu.tw)