0743 - 機率與統計
Probability and Statistics
教育目標 Course Target
1. Experiments, Models, and Probabilities
1) Applying Set Theory to Probability
2) Conditional Probability
3) Independence
2. Basics of Random Variables
1) Definitions
2) Probability Mass Function (PMF)
3) Families of Discrete Random Variables
4) Cumulative Distribution Function (CDF)
5) Probability Density Function (PDF)
6) Families of Continuous Random Variables
3. Random Variables and Expected Value
1) Conditional Probability Mass/Density Function
2) Probability Models of Derived Random Variables
3) Variance and Standard Deviation
4) Expected Value of a Derived Random Variable
4. Multiple Random Variables
1) Joint Cumulative Distribution Function
2) Joint Probability Mass/Density Function
3) Marginal PMF/PDF
4) Functions of Two Random Variables
5) Conditioning by a Random Variable
6) Independent Random Variables
5. Sums of Random Variables
1) Expected Values of Sums
2) PDF of the Sum of Two Random Variables
3) Moment Generating Functions
4) MGF of the Sum of Independent Random Variables
5) Random Sums of Independent Random Variables
6) Central Limit Theorem
7) Law of Large Numbers
1. Experiments, Models, and Probabilities
1) Applying Set Theory to Probability
2) Conditional Probability
3) Independence
2. Basics of Random Variables
1) Definitions
2) Probability Mass Function (PMF)
3) Families of Discrete Random Variables
4) Cumulative Distribution Function (CDF)
5) Probability Density Function (PDF)
6) Families of Continuous Random Variables
3. Random Variables and Expected Value
1) Conditional Probability Mass/Density Function
2) Probability Models of Derived Random Variables
3) Variance and Standard Deviation
4) Expected Value of a Derived Random Variable
4. Multiple Random Variables
1) Joint Cumulative Distribution Function
2) Joint Probability Mass/Density Function
3) Marginal PMF/PDF
4) Functions of Two Random Variables
5) Conditioning by a Random Variable
6) Independent Random Variables
5. Sums of Random Variables
1) Expected Values of Sums
2) PDF of the Sum of Two Random Variables
3) Moment Generating Functions
4) MGF of the Sum of Independent Random Variables
5) Random Sums of Independent Random Variables
6) Central Limit Theorem
7) Law of Large Numbers
參考書目 Reference Books
Probability and Stochastic Processes - A Friendly Introduction for Electrical and Computer Engineers," Second Edition
Probability and Stochastic Processes - A Friendly Introduction for Electrical and Computer Engineers," Second Edition
評分方式 Grading
| 評分項目 Grading Method |
配分比例 Percentage |
說明 Description |
|---|---|---|
|
課堂參與及作業 Class participation and work |
50 | |
|
期中課程評量成績 Midterm course evaluation results |
25 | |
|
期末課程評量成績 Final course evaluation results |
25 |
授課大綱 Course Plan
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課程資訊 Course Information
基本資料 Basic Information
- 課程代碼 Course Code: 0743
- 學分 Credit: 0-3
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上課時間 Course Time:Thursday/6,7,8,9,12,13[遠距課程]
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授課教師 Teacher:葉丙成
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修課班級 Class:共選修3,4(工學院開)
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選課備註 Memo:教育部補助臺灣大專院校人工智慧學程聯盟，開設學校：台灣大學，遠距課程，上課時間：週四下午 14:30-17:30 週四晚上 20:00-22:00。
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