|
課程名稱 |
機率與統計
PROBABILITY AND STATISTICS |
|
開課學期 |
98-2 |
|
授課對象 |
電機工程學系 |
|
授課教師 |
張時中 |
|
課號 |
EE2007 |
|
課程識別碼 |
901 21000 |
|
班次 |
03 |
|
學分 |
3 |
|
全/半年 |
半年 |
|
必/選修 |
必修 |
|
上課時間 |
星期一4(11:20~12:10)星期四7,8(14:20~16:20) |
|
上課地點 |
電二145電二145 |
|
備註 |
本系學生優先修習
總人數上限：70人 |
|
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/982prob_n_stats_scc |
|
課程簡介影片 |
|
|
核心能力關聯 |
核心能力與課程規劃關聯圖 |
|
課程大綱
|
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
|
課程概述 |
1. Experiments, Models, and Probabilities
2. Discrete Random Variables
3. Continuous Random Variables
4. Pairs of Random Variables
5. Random Vectors
6. Sums of Random Variables
7. Parameter Estimation Using the Sample Mean
8. Hypothesis Testing
|
|
課程目標 |
To introduce to students the theory, models and analysis of probability and basic statistics and their applications with emphasis on electrical and computer engineering problems.
|
|
課程要求 |
Grading: Homework : 20%, Midterm : 40%, Final : 40%, Participation 5% |
|
預期每週課後學習時數 |
|
|
Office Hours |
|
|
指定閱讀 |
|
|
參考書目 |
"Probability and Stochastic Processes - A Friendly
Introduction for Electrical and Computer Engineers," Second Edition
Authors : Roy D. Yates and David Goodman
Publisher : John Wiley & Sons, Inc., 2005. |
|
評量方式
(僅供參考) |
|
|
週次 |
日期 |
單元主題 |
|
Week 1 |
2/22,2/25 |
Motivation and Course Overview
Set Theory Review
Applying Set Theory to Probability
Probability Axioms |
|
Week 2 |
3/01,3/04 |
Probability Axioms (Cont.)
Some Consequences of the Axioms
Conditional Probability
Independence
Sequential Experiments
|
|
Week 3 |
3/08,3/11 |
Independence
Sequential Experiments
Counting Methods
Independent Trials
Reliability Methods
Discrete Random Variables
|
|
Week 4 |
3/15,3/18 |
DRVs: Probability Mass Function;
Family of D.R.Vs;
Cumulative Distribution Function;
Averages;
Reading Assignment: Sections 2.2-2.5 |
|
Week 5 |
3/22,3/25 |
Averages;
Functions of DRV;
Expected Value of a DRV;
|
|
Week 6 |
3/29,4/01 |
Variance and Standard Deviation (DRVs)
Conditional PMF (DRVs); |
|
Week 7 |
4/05,4/08 |
4/5 Holiday and no calss;
Continuous Random Variables (CRVs):
CDF;
Probability Density Functions (PDF);
Expected Values
Families of CRVs;
|
|
Week 8 |
4/12,4/15 |
Expected Values; Families of CRVs; Gaussian RVs;Delta Functions; Mixed Random Variables;
Probability Models of Derived Random Variables
|
|
Week 9 |
4/19,4/22 |
Probability Models of Derived Random Variables; Conditioning a CRV; 4/22 Midterm Exam (Chap. 1 – Chap. 3 |
|
Week 10 |
4/26,4/29 |
Pairs of R.Vs:
Joint CDF;
Joint PMF;
Marginal PMF;
Joint PDF;Marginal PDF; Functions of Two R.Vs;
|
|
Week 11 |
5/03,5/06 |
Functions of Two R.Vs (Cont.)
Expected Values;
Conditioning by an Event;Conditioning by a R.V.,; |
|
Week 12 |
5/10,5/13 |
Pairs of R.Vs:
Independent R.V.s,
Bivariate Gaussian R.V.s.
Random Vectors:
Probability Models of N Random Variables
(83de) Vector Notation
(83de) Marginal Probability Function
(83de) Independence |
|
Week 13 |
5/17,5/20 |
Random Vectors:
Independence;
Function of Random Vectors;
Expected Value Vector and Correlation Matrix;
Gaussian Random Vectors;
Sums of R. V.s:
Expected Values of Sums;
PDF of the Sum of Two R.V.s;
Reading Assignment: Sections 5.4-5.7, 6.1-6.2
|
|
Week 14 |
5/24,5/27 |
5/24: no class(done in advance)
5/27: Sums of R. V.s:
Expected Values of Sums;
PDF of the Sum of Two R.V.s;
Moment Generating Functions;
MGF of the Sum of Indep. R.Vs. |
|
Week 15 |
5/31,6/03 |
PDF of the Sum of Two R.V.s
Moment Generating Functions
MGF of the Sum of Indep. R.Vs
Random Sums of Indep. R.Vs
Sample Mean (7.1) |
|
Week 16 |
6/07,6/10 |
Deviation of R. V. from the Expected Value (7.2)
Point Estimate and Law of Large Numbers ( 7.3)
|
|
Week 17 |
6/14,6/17 |
Law of Large Numbers (Cont.)
Central Limit Theorem (6.6)
Application of CLT (6.7)
The Chernoff Bound (6.8)
Confidence Intervals
|
|
Week 18 |
|
Final Exam (Chaps. 4–7) |
|
Week 19 |
|
Significance Testing
Binary Hypothesis Testing
Multiple Hypothesis Test |
|