若欲以數學模型刻畫具有隨機特性的系統，並期待能據此數學模型作預測，則機率論將是最重要的數學工具之一，而機率論的建立又需要微積分、線性代數等數學數學工具。由機率論衍生的應用包括統計學、作業研究、網路通訊、量子物理、財務工程、人工智慧等。本課程的目標是引導同學學習機率論以下幾個重要的基本觀念，並熟練相關計算與應用：
1. 理解離散隨型機變數、機率質量函數、期望值等觀念，並且熟練其相關計算與應用。
2. 理解離連續型隨機變數、機率密度函數、期望值等觀念，並且熟練其相關計算與應用。
2. 理解離多變量隨機變數、聯合機率密度函數、邊際機率密度函數、條件期望值等觀念，並且熟練其相關計算與應用。
3. 理解隨機變數序列的收斂觀念，並且熟練其相關計算。If you want to use a mathematical model to engrave a system with random characteristics and look forward to predicting this mathematical model, chance theory will be one of the most important mathematical tools, and the establishment of chance theory requires mathematical mathematical tools such as micro-scores and linear generations. Applications derived from probability theory include statistics, operational research, network communication, quantum physics, financial engineering, artificial intelligence, etc. The purpose of this course is to guide students' learning chances to discuss the following basic concepts, and to be familiar with related calculations and applications:
1. Understand concepts such as dispersal machine variables, probability quality functions, and expected values, and be familiar with their related calculations and applications.
2. Understand the concepts of continuous random variables, rate density functions, expected values, etc., and be familiar with their related calculations and applications.
2. Understand the concepts of multiple variable random variables, combined rate density functions, side-by-side rate density functions, conditional expectations and other concepts, and be familiar with their related calculations and applications.
3. Understand the acceptance concept of random variable sequences and be familiar with their related calculations.
機率學主要的目的在於介紹和解析機會的結構及其相關之變數與函
數。
這方面的知識為許多進一步研究涉不確定性因素問題的學問的
基礎。本課程引導同學接觸一些有趣的理論和實例。
The main purpose of opportunity learning is to introduce and analyze the structure of opportunities and their related variables and functions.
Number.
This knowledge is a study of many further research on the problems of uncertain factors.
Basic. This course guides students to come across some interesting theories and examples.
1. Hossein Pishro-Nik, Introduction to Probability Statistics and Random Process.
附註：
1. 本書完整內容的html版(免費)連結為http://www.probabilitycourse.com.
2. 因為紙本版未透過出版商發行，如果要購買紙本，請自行至amazon購買， 另外完整習題解答的紙本版，也可以在 amazon購買。
1. Hossein Pishro-Nik, Introduction to Probability Statistics and Random Process.
Note:
1. The html version (free) link of the complete content of this book is http://www.probabilitycourse.com.
2. Because the paper version has not been released through the publisher, if you want to buy the paper, please go to Amazon to buy it yourself. In addition, the paper version with complete questions can also be purchased on Amazon.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 平時成績平時成績 Regular achievements |
30 | 包含作業、助教隨堂測驗等成績 |
| 考試考試 exam |
80 | 包括二次小考、期中考、期末考，每次20% |
