機率
Probability
學期
112-2
學分
0
學分
當期課號
515002
永久課號
EEEC10006
開課單位
電機共同課程
授課教師
林詩淳
校區
光復
類別
必修
上課時間表
| 節 | 週二 | 週五 |
|---|---|---|
2 09:00–09:50 | 機率 ED301（光復） | |
5 13:20–14:10 | 機率 ED301（光復） 2 節連堂 | |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
概述
Probability and statistics: concepts and calculations.
先修科目
Linear Algebra, Calculus
備註
無備註
教學方式
E3 NYCU web
評分方式
Assignments: 10 %, Midterm:40%, Final : 50%
課程大綱
教師未提供此項資料
週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | 1.1 Sets1.2 Probability Models1.3 Conditional Probability1.4 Total Probability Theorem and Bayes’ Rule1.5 Independence1.6 Counting |
| 第 2 週 | 1.1 Sets1.2 Probability Models1.3 Conditional Probability1.4 Total Probability Theorem and Bayes’ Rule1.5 Independence1.6 Counting |
| 第 3 週 | 1.1 Sets1.2 Probability Models1.3 Conditional Probability1.4 Total Probability Theorem and Bayes’ Rule1.5 Independence1.6 Counting |
| 第 4 週 | 2.1 Basic Concepts2.2 Probability Mass Functions2.3 Functions of Random Variables2.4 Expectation, Mean, and Variance2.5 Joint PMFs of Multiple Random Variables2.6 Conditioning2.7 Independence |
| 第 5 週 | 2.1 Basic Concepts2.2 Probability Mass Functions2.3 Functions of Random Variables2.4 Expectation, Mean, and Variance2.5 Joint PMFs of Multiple Random Variables2.6 Conditioning2.7 Independence |
| 第 6 週 | 2.1 Basic Concepts2.2 Probability Mass Functions2.3 Functions of Random Variables2.4 Expectation, Mean, and Variance2.5 Joint PMFs of Multiple Random Variables2.6 Conditioning2.7 Independence |
| 第 7 週 | 3.1 Continuous Random Variables and PDFs3.2 Cumulative Distribution Functions3.3 Normal Random Variables3.4 Joint PDFs of Multiple Random Variables3.5 Conditioning3.6 The Continuous Bayes’ Rule |
| 第 8 週 | 3.1 Continuous Random Variables and PDFs3.2 Cumulative Distribution Functions3.3 Normal Random Variables3.4 Joint PDFs of Multiple Random Variables3.5 Conditioning3.6 The Continuous Bayes’ Rule |
| 第 9 週 | 3.1 Continuous Random Variables and PDFs3.2 Cumulative Distribution Functions3.3 Normal Random Variables3.4 Joint PDFs of Multiple Random Variables3.5 Conditioning3.6 The Continuous Bayes’ Rule |
| 第 10 週 | 4.1 Derived Distributions4.2 Covariance and Correlation4.3 Conditional Expectation and Variance Revisited4.4 Transforms4.5 Sum of a Random Number of Independent Random Variables |
| 第 11 週 | 4.1 Derived Distributions4.2 Covariance and Correlation4.3 Conditional Expectation and Variance Revisited4.4 Transforms4.5 Sum of a Random Number of Independent Random Variables |
| 第 12 週 | 4.1 Derived Distributions4.2 Covariance and Correlation4.3 Conditional Expectation and Variance Revisited4.4 Transforms4.5 Sum of a Random Number of Independent Random Variables |
| 第 13 週 | 5.1 Markov and Chebyshev Inequalities5.2 The Weak Law of Large Numbers5.3 Convergence in Probability5.4 The Central Limit Theorem5.5 The Strong Law of Large Numbers |
| 第 14 週 | 5.1 Markov and Chebyshev Inequalities5.2 The Weak Law of Large Numbers5.3 Convergence in Probability5.4 The Central Limit Theorem5.5 The Strong Law of Large Numbers |
| 第 15 週 | Optional Topics |
| 第 16 週 | Optional Topics |
| 第 17 週 | |
| 第 18 週 |
教科書
Probability & Stochastic Process 3/e, Yates
Office Hours
- 地點
- ED503
- 時間
- by appointment
- 聯絡方式
- hdtd5746@gmail.com