課程資訊
課程名稱
 機率與統計
Probability and Statistics 
開課學期
 99-2 
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
 901E21000 
班次
 05 
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
 電二101電二101 
備註
 本課程以英語授課。本系學生優先修習
總人數上限：30人 
Ceiba 課程網頁
 http://ceiba.ntu.edu.tw/992_prob_stat_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
 Textbook： "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)
    
課程進度
週次
日期
單元主題
Week 1
 2/21,2/24  Motivation and Course Overview Set Theory Review Applying Set Theory to Probability Probability Axioms 
Week 2
 2/28,3/03  228 National Holiday
Probability Axioms (Cont.) Some Consequences of the Axioms Conditional Probability Independence  
Week 3
 3/07,3/10  Sequential Experiments Counting Methods Independent Trials Reliability Methods Discrete Random Variables  
Week 4
 3/14,3/17  Lecturer on trip to Univ. of Connecticut for Induction Ceremony to Academy of Distinguished Engineers; The three hour class is made up in Thursdays in the weeks of 2/28, 4/4, and 6/6.  
Week 5
 3/21,3/24  Discrete Random Variables:
Probability Mass Function
Family of D.R.Vs
Cumulative Distribution Function
Averages
 
Week 6
 3/28,3/31  Averages (Cont.)
Functions of DRV
Expected Value of a DRV
Variance and Standard Deviation
 
Week 7
 4/04,4/07  Variance and Standard Deviation (DRVs);
Conditional PMF (DRVs); Continuous Random Variables;
CDF;
Probability Density Functions (PDF);
 
Week 8
 4/11,4/14  Probability Density Functions (Cont.);
Expected Values;
Gaussian R.Vs; 
Week 9
 4/18,4/21  Gaussian R.Vs(Cont.);Families of CRVs; Delta Functions, Mixed Random Variables; Probability Models of Derived Random Variables (Cont.);
Conditioning a CRV;
 
Week 10
 4/25,4/28  4/28 mid term exam (Chap 1-3);
 
Week 11
 5/02,5/05  Pairs of R.Vs.:Joint CDF;Joint PMF;Marginal PMF;Joint PDF;
Marginal PDF;
Function of two R.Vs 
Week 12
 5/09,5/12  Functions of Two R.Vs;Expected Values; 
Week 13
 5/16,5/19  Pairs of R.Vs.
Conditioning by an Event;
Conditioning by a random variable;
Independent random variables;Bivariate Gaussian R.V.s
Random Vectors:
Probability Models of N Random Variables
Vector Notation
 
Week 14
 5/23,5/26  Random Vectors:
Probability Models of N Random Variables;
Vector Notation;
Marginal Probability Function;
Independence;
Function of Random Vectors;
Expected Value Vector and Correlation Matrix;
Gaussian Random Vectors;
 
Week 15
 5/30,6/02  Random Vectors:
Gaussian Random Vectors
Sum of Random Variables:
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 16
 6/06,6/09  MGF of the Sum of Indep. R.Vs (Cont.);
Random Sums of Indep. R.Vs
Sample Mean (7.1)
Deviation of R. V. from the Expected Value (7.2)
 
Week 17
 6/13,6/16  Sums of R.Vs & Parameter Est.:
Point Estimates of Model Parameters &
Law of Large Numbers (7.3);
Central Limit Theorem (6.6);
Application of CLT (6.7);
The Chernoff Bound (6.8);