機率(英文班)
Probability
| 節 | 週二 | 週五 |
|---|---|---|
2 09:00–09:50 | 機率(英文班) ED103（光復） | |
5 13:20–14:10 | 機率(英文班) ED103（光復） 2 節連堂 | |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
Teach the theory and applications of calculus-based probability theory. Applications of probability theory include but are not limited to wireless communication systems, computer networking, and machine learning.
calculus, linear algebra, and computer programming (such as C/C++, Python, or Matlab for homework).
無備註
Lectures. Office hour: Friday, 10am-10:50am.
Homework and in-class performance: 20% Midterm Exam: 40% Final Exam: 40%
Sample Space and Probability
Chapter 1
- 講授：
- 6
Discrete Random Variables
Chapter 2
- 講授：
- 6
Continuous and General Random Variables
Chapter 3: probability density function, exponential random variables, Gaussian random variables, four Bayes' rules and etc.
- 講授：
- 9
Further Topics on Random Variables
Chapter 4: derived random variables, moment generating function, conditional expectation as a random variable.
- 講授：
- 9
Limit Theorems:
Chapter 5: Markov inequality, law of large numbers, central limit theorem.
- 講授：
- 9
An Introduction to Discrete-Time Markov Chains
7.1 Discrete-Time Markov Chains and examples 7.3 Steady-state behavior
- 講授：
- 3
-
| 週次 | 主題 |
|---|---|
| 第 1 週 | Chapter 1: sample space and probability, conditional probability and independenceChapter 2: discrete random variables, probability mass function 2026-02-24(二),2026-02-27(五) |
| 第 2 週 | Chapter 2: functions of random variables, expectation and variance 2026-03-03(二),2026-03-06(五) |
| 第 3 週 | Chapter 2: joint PMF, conditioning independence 2026-03-10(二),2026-03-13(五) |
| 第 4 週 | Chapter 3: continuous random variables, cumulative distribution functions, probability density functions. 2026-03-17(二),2026-03-20(五) |
| 第 5 週 | Chapter 3: Normal/Gaussian random variables. 2026-03-24(二),2026-03-27(五) |
| 第 6 週 | Chapter 3: joint PDFs of multiple random variables, conditioning for continuous random variables. 2026-03-31(二),2026-04-03(五) |
| 第 7 週 | Chapter 3: The continuous Bayes' rules 2026-04-07(二),2026-04-10(五) |
| 第 8 週 | Midterm Exam 2026-04-14(二),2026-04-17(五) |
| 第 9 週 | Chapter 4 (4.1 and 4.2): Derived distributions, covariance and correlation 2026-04-21(二),2026-04-24(五) |
| 第 10 週 | Chapter 4 (4.4): Transforms 2026-04-28(二),2026-05-01(五) |
| 第 11 週 | Chapter 4 (4.3): conditional expectation and variance as random variables 2026-05-05(二),2026-05-08(五) |
| 第 12 週 | Chapter 4 (4.5): Sum of a random number of independent random variables 2026-05-12(二),2026-05-15(五) |
| 第 13 週 | Chapter 5: Markov and Chebyshev inequalities, The weak law of large numbers, Convergence in probability. 2026-05-19(二),2026-05-22(五) |
| 第 14 週 | Chapter 5: Central limit theorem, the strong law of large numbers. 2026-05-26(二),2026-05-29(五) |
| 第 15 週 | Chapter 7 (7.1 and 7.3): Discrete-time Markov chains 2026-06-02(二),2026-06-05(五) |
| 第 16 週 | Final exam 2026-06-09(二),2026-06-12(五) |
Introduction to Probability, 2nd Edition, D. P. Bertsekas and J. N. Ysitsiklis, Athena Scientific, 2008.
- 地點
- ED730
- 時間
- 5CD
- 聯絡方式
- Email: gaurunghung@nycu.edu.tw