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課程名稱 |
機率與統計
Probability and Statistics |
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開課學期 |
101-2 |
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授課對象 |
電機工程學系 |
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授課教師 |
張時中 |
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課號 |
EE2007 |
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課程識別碼 |
901E21000 |
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班次 |
03 |
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學分 |
3 |
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全/半年 |
半年 |
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必/選修 |
必修 |
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上課時間 |
星期一4(11:20~12:10)星期四7,8(14:20~16:20) |
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上課地點 |
電二145電二145 |
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備註 |
本課程以英語授課。本系學生優先修習
總人數上限：70人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1012prob_stats |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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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
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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.
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課程要求 |
Grading: Homework : 20%, Midterm : 40%, Final : 40%, Participation 5% |
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預期每週課後學習時數 |
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Office Hours |
每週四 12:30~13:30
每週一 12:10~13:10 備註： TBD |
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指定閱讀 |
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參考書目 |
教科書： "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. |
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評量方式
(僅供參考) |
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週次 |
日期 |
單元主題 |
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Week 1 |
2/18,2/21 |
1.0 Motivation and Course overview
1.1 Applying Set Theory to Probability
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Week 2 |
2/25,2/28 |
1.2 Probability Axioms
1.3 Some Consequences of the Axioms
228 National Memorial Day-no class |
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Week 3 |
3/04,3/07 |
1.4 Some Consequences of the Axioms (Cont.);
1.5 Conditional Probability;
1.6 Independence
1.7 Sequential Experiments and Tree Diagrams
2.1 Discrete Random Variables: Definitions
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Week 4 |
3/11,3/14 |
Discrete Random Variables:
Probability Mass Function
Family of D.R.Vs
Cumulative Distribution Function
Averages
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Week 5 |
3/18,3/21 |
Families of Discrete Random Variables (Cont.);
Cumulative Distribution Function (CDF):
DRV;
CRV;
Probability Density Function.
Reading Assignment: Sections 2.3, 2.4, 3.1, 3.2
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Week 6 |
3/25,3/28 |
Probability Density Function (Cont.) (3.2);
Families of Continuous Random Variables (3.4, 3.5);
Families of Continuous Random Variables (3.4, 3.5);
Reading Assignment: Sections 2.6, 3.2-3.5
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Week 7 |
4/01,4/04 |
Gaussian Random Variables (3.5);
Conditional Probability Mass/Density Function (2.9, 3.8);
Reading Assignment: Sections 3.5, 2.9, 3.8
4/04 Spring Review Break; no class.
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Week 8 |
4/08,4/11 |
Conditional Probability Mass/Density Function (2.9, 3.8);
Probability Models of Derived Random Variables (2.6, 3.7);
Averages (2.5, 2.7, 3.3);
Variance and Standard Deviation (2.8).
Reading Assignment: Sections 2.5, 2.7-2.9, 3.3, 3.7-3.9
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Week 9 |
4/15,4/18 |
Probability Models of Derived Random Variables (2.6, 3.7);
Expected Value of a Derived Random Variable (2.7);
Variance and Standard Deviation (2.8);
Midterm Exam (4/18, 3:30pm-)
Reading Assignment: Sections Chaps. 1-3
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Week 10 |
4/22,4/25 |
Random Vector:
Probability Models of N Random Variables;
Vector Notation;
Pairs of R.Vs.:
Joint CDF;
Joint PMF;
Reading Assignment: Sections 5.1, 5.2, 4.1-4.2
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Week 11 |
4/29,5/02 |
Pairs of R.Vs.:
Marginal PMF (Cont.);
Joint PDF;
Marginal PDF;
Functions of Two R.Vs;
Expected Values;
Reading Assignment: Sections 4.3-4.9
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Week 12 |
5/06,5/09 |
Pairs of R.Vs.(Cont.):
Expected Values;
Conditioning by an Event;
Conditional PDF;
Reading Assignment: Sections 4.7-4.9.
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Week 13 |
5/13,5/16 |
Pair of Random Variables and Vectors:
Independence between Two R.Vs;
Bivariate R.V.s and Vector Expressions;
Vector Functions of Random Vectors: n=2;
Reading assignment: 4.10-11; Chapter 5.
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Week 14 |
5/20,5/23 |
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;
Reading Assignment:
Textbook Sections 6.1-6.4
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Week 15 |
5/27,5/30 |
Sums of R. V.s (Cont.):
MGF of the Sum of Indep. R.Vs;
Random Sums of Indepent R.Vs;
Central Limit Theorem;
Application of the Central Limit Theorem;
The Chernoff Bound.
Reading Assignment: 6.4 – 6.8
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Week 16 |
6/03,6/06 |
Sums of R. V.s (Cont.):
Application of the Central Limit Theorem (Cont.);
The Chernoff Bound.
Parameter Estimation Using the Sample Mean:
Sample Mean: Expected Value and Variance;
Deviation of a Random Variable from the Expected Value;
Point Estimates of Model Parameters;
Confidence Intervals.
Reading Assignment: 6.7 – 6.8, 7.1 – 7.4
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Week 17 |
6/10,6/13 |
Parameter Estimation Using the Sample Mean:
Confidence Intervals (Cont.);
Significance Testing.
Reading Assignment: 7.4, 8.1
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Week 18 |
06/17, 20 |
Final Exam (6/20, 15:30, See details at https://sites.google.com/site/ntueepas2013/)
Scope: Chapters 4, 5, 6 and Secs 7.1-7.4
(May skip the parts about Matrix Form representations of 3 or more r.vs)
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Week 19 |
06/24 |
Makeup Lecture (14 – 16, 6/24; Rm. 145, EE-2 Bldg.):
Significance Testing (Cont.);
Binary Hypothesis Testing.
Reading Assignment: 8.1 – 8.2
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