查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

110-1 293657 隨機信號與系統 1 3.0 3 ★ 吳和庭資工所
電資外生所 6 0 資工所和電資國際班合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
110-1	293657	隨機信號與系統	1	3.0	3	★	吳和庭	資工所電資外生所	6	0	資工所和電資國際班合開

教學大綱與進度：

教師姓名吳和庭

Email htwu@ntut.edu.tw

最後更新時間 2021-09-17 15:41:38

課程大綱 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. This course offers substantial probabilistic and statistical backgrounds for the machine learning (ML) field and serves as a basic course in field of communications, networks and controls in graduate school.

課程進度 Course Website: https://myweb.ntut.edu.tw/~htwu/courses/Fall2021RP/Fall2021RP.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. Sums of Random Variables Week 8. Moment Generating Functions, Central Limit Theorem Week 9. Midterm; 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. 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

評量方式與標準 1. Homeworks + Class Participation 40% 2. Midterm Exam 30% 3. Final Exam. 30%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
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

課程諮詢管道教師研究室: 宏裕科技大樓　1528 室；(O)(02)27712171 x 4221
Office location: room# 1528, Technology Building. (O)(02)27712171 x 4221

備註 ●上課方式：遠距上課

●評量方式：
原則上期中考、期末考採實體紙筆測驗。
In general, the midterm and final exams will take place in classroom, even if the remote teaching format is conducted.
●補充說明資訊：
The distance learning approach will be adopted during the first 3 week period from 9/22 to 10/10, using Microsoft (MS) Teams Platform. Please refer to the course website for detailed info.

教師姓名	吳和庭
Email	htwu@ntut.edu.tw
最後更新時間	2021-09-17 15:41:38
課程大綱	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. This course offers substantial probabilistic and statistical backgrounds for the machine learning (ML) field and serves as a basic course in field of communications, networks and controls in graduate school.
課程進度	Course Website: https://myweb.ntut.edu.tw/~htwu/courses/Fall2021RP/Fall2021RP.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. Sums of Random Variables Week 8. Moment Generating Functions, Central Limit Theorem Week 9. Midterm; 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. 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
評量方式與標準	1. Homeworks + Class Participation 40% 2. Midterm Exam 30% 3. Final Exam. 30%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 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
課程諮詢管道	教師研究室: 宏裕科技大樓　1528 室；(O)(02)27712171 x 4221 Office location: room# 1528, Technology Building. (O)(02)27712171 x 4221
備註	●上課方式：遠距上課 ●評量方式：原則上期中考、期末考採實體紙筆測驗。 In general, the midterm and final exams will take place in classroom, even if the remote teaching format is conducted. ●補充說明資訊： The distance learning approach will be adopted during the first 3 week period from 9/22 to 10/10, using Microsoft (MS) Teams Platform. Please refer to the course website for detailed info.