Course Information
Course title
Probability and Statistics 
Semester
104-2 
Designated for
DEPARTMENT OF ELECTRICAL ENGINEERING  
Instructor
SHI CHUNG CHANG 
Curriculum Number
EE2007 
Curriculum Identity Number
901 21000 
Class
04 
Credits
Full/Half
Yr.
Half 
Required/
Elective
Required 
Time
Monday 4(11:20~12:10) Thursday 8,9(15:30~17:20) 
Remarks
The upper limit of the number of students: 50. 
Ceiba Web Server
http://ceiba.ntu.edu.tw/1042_Prob_E 
Course introduction video
 
Table of Core Capabilities and Curriculum Planning
Table of Core Capabilities and Curriculum Planning
Course Syllabus
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Course Description

Tentative Course Outline:

1. Experiments, Models, and Probabilities
1.1. Applying Set Theory to Probability
1.2. Probability Axioms
1.3. Some Consequences of the Axioms
1.4. Conditional Probability
1.5. Independence
1.6. Sequential Experiments and Tree Diagrams

2. Random Variables
2.1. Definitions
2.2. Probability Mass Function
2.3. Families of Discrete Random Variables
2.4. Cumulative Distribution Function (CDF)
2.5. Probability Density Function
2.6. Families of Continuous Random Variables

3. Random Variables and Expected Value
3.1. Conditional Probability Mass/Density Function
3.2. Probability Models of Derived Random Variables
3.3. Average
3.4. Variance and Standard Deviation
3.5. Expected Value of a Derived Random Variable

Midterm exam

4. Random Vectors
4.1. Probability Models of N Random Variables
4.2. Vector Notation
4.3. Joint Cumulative Distribution Function
4.4. Joint Probability Mass/Density Function
4.5. Marginal PMF/PDF
4.6. Functions of Two Random Variables (Jacobian Transformation)
4.7. Conditioning by a Random Variables
4.8. Bivariate Gaussian Random Variables
4.9. Correlation Matrix

5. Sums of Random Variables
5.1. Expected Values of Sums
5.2. PDF of the Sum of Two Random Variables
5.3. Moment Generating Functions
5.4. MGF of the Sum of Independent Random Variables
5.5. Random Sums of Independent Random Variables
5.6. Central Limit Theorem
5.7. Applications of the Central Limit Theorem
5.8. The Chernoff Bound

6. Parameter Estimation Using the Sample Mean
6.1. Sample Mean: Expected Value and Variance
6.2. Deviation of a Random Variable from the Expected Value
6.3. Point Estimates of Model Parameters
6.4. Confidence Intervals

7. Hypothesis Testing
7.1. Significance Testing
7.2. Binary Hypothesis Testing

Final exam 

Course Objective
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. 
Course Requirement
Calculus (A) 1 & 2 
Office Hours
每週一 12:10~13:10
每週四 12:30~13:30 Note: TBD 
References
Will be provided in class. 
Designated reading
Probability and Stochastic Processes (2nd Edition) by R.D. Yates, D.J. Goodman, John Wiley and Sons, 2005 
Grading
 
No.
Item
%
Explanations for the conditions
1. 
Participation 
5% 
 
2. 
Homework and recitation problems 
25% 
 
3. 
Final exam 
35% 
 
4. 
Midterm exam 
35% 
 
 
Progress
Week
Date
Topic
Week 1
02/22, 25  1.1 Motivation and Course overview 1.2 Applying Set Theory to Probability 1.3 Probability Axioms 
Week 2
03/03  1.3 Probability Axioms (Cont.) 1.4 Some Consequences of the Axioms 1.5 Conditional Probability; 
Week 3
03/07, 03/10  1.6 Independence Chapter 2 Sequential Experiments and Tree Diagrams 3.1 Discrete Random Variables: 3.2 Definitions of Probability Mass Function Reading Assignment: Chapter 2 and Sections 3.1, 3.2 
Week 4
03/13, 03/17  Definition of Random Variables DRVs (Textbook 3.1) CRVs (Textbook 4.1) Probability Mass Function (Textbook 3.2) Families of Discrete Random Variables (Textbook 3.3) Reading Assignment: Sections 3.1-3.4, 4.1-4.2 Recitation 2: BL 113, 18:00-19:00, 3/16/2016 
Week 5
03/21, 03/24  Probability Mass Function of DRVs (Cont.) Cumulative Distribution Functions (CDF) DRVs (Textbook 3.4) Definition and CDF of CRVs (Textbook 4.1) Probability Density Function (4.3 in 3rd Edition) Families of Continuous Random Variables (4.5 in 3rd Edition) Reading Assignment: Sections 4.1~4.3,4.5 No Recitation this week! 
Week 6
03/28, 03/31  Uniform Random Variables  (Cont.) and Generation (4.4) Averages and Expected Values of R. Vs. (3.5, 4.4) Variance and Standard Deviation (3.8) Families of Continuous Random Variables (4.5) Functions of a Random Variable (3.6) Reading Assignment: Sections 3.5, 3.6, 4.4, 4.5 Recitation 3: BL 113, 18:00-19:00, 3/30/2016 
Week 7
04/07  Families of Continuous Random Variables (Cont., 4.5) Gaussian Random Variables (4.6) Functions of a Random Variable (3.6) Probability Models of Derived R.V. (6.2) Reading Assignment: Sections 3.6, 4.5~4.6, 6.2 Recitation 4: BL 113, 18:00-19:00, 4/6/2016 
Week 8
4/11, 4/14  Gaussian Random Variables (Cont., 4.5~4.6) Functions of a Random Variable (3.6) Probability Models of Derived R.V. (6.2) Reading Assignment: Sections 3.6, 4.5~4.6, 6.2 Recitation 5: BL 113, 18:00-19:00, 4/13/2016 
Week 9
4/18, 4/21  Probability Models of Derived R.V. (Cont.) DRV (3.6) CRV (6.2) MRV (4.7, 6.3) Random Variable Conditioned on an Event (7.1) Conditional Expected Value Given an Event (7.2) Reading Assignment: Sections 3.6, 6.2~6.3, 7.1~7.2 Previous midterm test sets Recitation 6: BL 113, 18:00-19:00, 4/20/2016 
Week 10
05/02, 05/05  Multiple Random Variables Joint CDF Joint PMF Marginal PMF Joint pdf Marginal pdf Independent R.Vs. Reading Assignment: Sections 5.1~ 5.6 
Week 10
4/25, 4/28  Joint CDF (5.1) Midterm Exam (4/28) !!! Reading Assignment: Chapters 1~4 (excluding Sec. 4.7) Sections: 6.2~6.3, 7.1~7.2 Recitation 7: BL 113, 18:00-19:00, 4/27/2016 
Week 11
05/09, 05/12  Pairs of Random Variables Joint pdf (Cont.) Marginal pdf Independent R.Vs. Expected Values of a Function of Two R.Vs Co-variance, Correlation and Independence Bivariate Gaussian R. Vs. Reading Assignment: Sections 5.6 ~ 5.9 
Week 12
05/16, 05/19  Pairs of Random Variables Co-variance, Correlation Bivariate Gaussian R. Vs. PMF of a Function of Two Discrete Random Variables (Sec. 6.1) Continuous Functions of Two Continuous Random Variables (Sec. 6.4) PDF of the Sum of Two Random Variables (Sec. 6.5) Reading Assignment: Sections 5.8 ~ 5.9, 6.1, 6.4~6.5 
Week 13
05/23, 05/26  Pairs of Random Variables Continuous Functions of Two Continuous Random Variables (Sec. 6.4, Cont.) PDF of the Sum of Two Random Variables (Sec. 6.5) Conditioning Two Random Variables by an Event (Sec. 7.3) Conditioning by a Random Variable (Sec. 7.4) Conditional Expected Value (Sec. 7.5) Conditional PDF of Bivariate Gaussian Reading Assignment: Sections 6.4~6.5, 7.3~7.6 
Week 14
05/30, 06/02  Sum of Random Variables Conditional PDF of Bivariate Gaussian (Cont.) Expected Values of Sum Moment Generating Functions MGF of the Sum of Indep. R.Vs. Reading Assignment: Sections 7.6, 9.1 ~ 9.3 
Week 15
0606, 0612  This Week Sum of Random Variables (Cont.) - MGF of Random Sum of Indep. R.Vs. - Central Limit Theorem and Applications Deviation of a Random Variable from the Expected Value Reading Assignment: Sections 9.4, 10.2 & Supplements 
Week 16
0613, 0616  Binary Hypothesis Testing - Tests, Likelihood and Types of Errors - MAP Test - Minimum Cost Test - Maximum Likelihood Test Reading Assignment: Section 11.1