Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2020 278243 Random Signals and Systems 1 3.0 3 ★ HO-TING WU GCSIE
IEECSG 12 1 ◎

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2020	278243	Random Signals and Systems	1	3.0	3	★	HO-TING WU	GCSIE IEECSG	12	1	◎

Course Plan：

Instructor HO-TING WU

Email htwu@ntut.edu.tw

Last Updated 2020-09-10 17:19:29

Course Objective This course contains the basic theories in probability and random processes. It includes probability measures, random variables, the law of large number, statistical inference, random processes and stationary processes. The course is a basic course in field of communications and controls in graduate school.

Course Schedule
(Week 1-16) Course Website: https://myweb.ntut.edu.tw/~htwu/courses/Fall2020RP/Fall2020RP.htm Week 1. Reviews of Probability Concepts Week 2. Discrete and Continuous Random Variables Week 3. Sequential Events, Conditional Probability and Conditional Expectation Week 4. Multiple Random Variables Week 5. Functions of Random Variables Week 6. Random Vectors Week 7. Midterm 1; Sums of Random Variables Week 8. Moment Generating Functions, Central Limit Theorem Week 9. The Sample Mean Week 10. Point Estimates of the Model Parameter Week 11. Confidence Intervals; Significance Testing Week 12. Binary and Multiple Hypothesis Testing Week 13. Midterm 2; Estimation of Random Variables Week 14. Optimal Estimation; MAP and ML Estimation Week 15. Definitions and Types of Random Processes Week 16. The Poisson Process; The Brownian Process Week 17. Stationary Random Processes Week 18. Final Exam

Evaluation and grading policy 1. Homeworks + Class Participation 20% 2. Two Midterm Exams 25%*2=50% 3. Final Exam. 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Prerequisites: Probability, Transform Theory Textbook: Yates and Goodman, “Probability and Stochastic Processes,” 2nd Ed., John Wiley & Sons, 2005. (You may choose the 3rd ed. of this textbook, which contains similar contents and was published in 2015.) References: 1. Leon-Garcia, “Probability and Random Processes for Electrical Engineering,” 3rd ed., Pearson, 2009. 2. Childers, “Probability and Random Processes”, Irwin, 1997. 3. Papoulis and Pillai, “Probability, Random Variables, and Stochastic Processes,” 4th ed., Mc-Graw-Hill, 2002. 4. Stark and Woods, “Probability and Random Processes with application to Signal Processes”, 3rd ed., Prentice Hall, 2002. 5. Ross, “Introduction to Probability Models”, 7th ed., Academic Press, 2000. 6. Ghahramani, “Fundamentals of Probability with Stochastic Processes”, 3rd ed., Prentice Hall, 2005. 7. Bhat and Miller, “Elements and Applied Stochastic Processes”, 3rd ed., 2002. 8. Ross, “A First Course in Probability”, 7th ed., Prentice -Hall, 2006

The access to curricular consultation

Expanding teaching and resources

The course corresponds to the SDGs

Does the course incorporate AI

Note
　

Instructor	HO-TING WU
Email	htwu@ntut.edu.tw
Last Updated	2020-09-10 17:19:29
Course Objective	This course contains the basic theories in probability and random processes. It includes probability measures, random variables, the law of large number, statistical inference, random processes and stationary processes. The course is a basic course in field of communications and controls in graduate school.
Course Schedule (Week 1-16)	Course Website: https://myweb.ntut.edu.tw/~htwu/courses/Fall2020RP/Fall2020RP.htm Week 1. Reviews of Probability Concepts Week 2. Discrete and Continuous Random Variables Week 3. Sequential Events, Conditional Probability and Conditional Expectation Week 4. Multiple Random Variables Week 5. Functions of Random Variables Week 6. Random Vectors Week 7. Midterm 1; Sums of Random Variables Week 8. Moment Generating Functions, Central Limit Theorem Week 9. The Sample Mean Week 10. Point Estimates of the Model Parameter Week 11. Confidence Intervals; Significance Testing Week 12. Binary and Multiple Hypothesis Testing Week 13. Midterm 2; Estimation of Random Variables Week 14. Optimal Estimation; MAP and ML Estimation Week 15. Definitions and Types of Random Processes Week 16. The Poisson Process; The Brownian Process Week 17. Stationary Random Processes Week 18. Final Exam
Evaluation and grading policy	1. Homeworks + Class Participation 20% 2. Two Midterm Exams 25%*2=50% 3. Final Exam. 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Prerequisites: Probability, Transform Theory Textbook: Yates and Goodman, “Probability and Stochastic Processes,” 2nd Ed., John Wiley & Sons, 2005. (You may choose the 3rd ed. of this textbook, which contains similar contents and was published in 2015.) References: 1. Leon-Garcia, “Probability and Random Processes for Electrical Engineering,” 3rd ed., Pearson, 2009. 2. Childers, “Probability and Random Processes”, Irwin, 1997. 3. Papoulis and Pillai, “Probability, Random Variables, and Stochastic Processes,” 4th ed., Mc-Graw-Hill, 2002. 4. Stark and Woods, “Probability and Random Processes with application to Signal Processes”, 3rd ed., Prentice Hall, 2002. 5. Ross, “Introduction to Probability Models”, 7th ed., Academic Press, 2000. 6. Ghahramani, “Fundamentals of Probability with Stochastic Processes”, 3rd ed., Prentice Hall, 2005. 7. Bhat and Miller, “Elements and Applied Stochastic Processes”, 3rd ed., 2002. 8. Ross, “A First Course in Probability”, 7th ed., Prentice -Hall, 2006
The access to curricular consultation
Expanding teaching and resources
The course corresponds to the SDGs
Does the course incorporate AI
Note