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 describe a system with random characteristics and expect to make predictions based on this mathematical model, probability theory will be one of the most important mathematical tools, and the establishment of probability theory requires mathematical tools such as calculus and linear algebra. Applications derived from probability theory include statistics, job research, network communications, quantum physics, financial engineering, artificial intelligence, etc. The goal of this course is to guide students to learn the following important basic concepts of probability theory and become proficient in related calculations and applications:
1. Understand concepts such as discrete random machine variables, probability mass functions, and expected values, and be proficient in their related calculations and applications.
2. Understand concepts such as discrete random variables, probability density functions, and expected values, and be proficient in their related calculations and applications.
2. Understand concepts such as discrete multivariable random variables, joint probability density function, marginal probability density function, conditional expected value, etc., and be proficient in their related calculations and applications.
3. Understand the concept of convergence of random variable sequences and be proficient in its 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 probability science is to introduce and analyze the structure of chance and its related variables and functions.
Count.
This knowledge provides a basis for further research on issues involving uncertainty factors.
Basics. This course introduces students to 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。

3. Robert V. Hogg, Elliot A. Tanis, and Dale L. Zimmerman, Probability and Statistical Inference, 9th Edition, Global Edition, Pearson. （華泰書局代理）

1. Hossein Pishro-Nik, Introduction to Probability Statistics and Random Process.

Note:
1. The link to the html version (free) of the complete content of this book is http://www.probabilitycourse.com.
2. Because the paper version is not distributed through the publisher, if you want to purchase the paper version, please go to Amazon to purchase it. In addition, the paper version of the complete exercise solutions can also be purchased on Amazon.

3. Robert V. Hogg, Elliot A. Tanis, and Dale L. Zimmerman, Probability and Statistical Inference, 9th Edition, Global Edition, Pearson. (Agent by Huatai Book Company)