機率學(Probability)自20世紀起一直是各類應用工程非常重要的領域知識，近年更大量應用至AI領域的模型建置。
在電腦承擔大量運算以及套裝軟體或程式簡化操作過程的現代，學習機率學有助於學員理解以及解構問題本身。
進而選擇並正確使用數學模型、AI工具、套裝軟體以及最後的結果呈現
本課程預期在在一學期的時間內教授以下內容
1.試驗、模型與機率
2.隨機變數(離散、連續)
3.條件機率與相關AI模型
部分課程將搭配 python/matlab/excel範例Probability has been a very important domain knowledge for various application projects since the 20th century. In recent years, more large number of models have been applied to the AI domain.
In the modern era where computers undertake large amounts of computing and simplified operational processes of packaged software or program, learning probability learning helps students understand and solve problems themselves.
Then choose and use mathematical models, AI tools, package software and final results are presented
This course is expected to teach the following content during the first session
1.Test, model and chance
2. Random variables (dispersed, continuous)
3. Conditional probability and related AI models
Some courses will be matched with python/matlab/excel examples
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. 機率學(Probability and Stochastic Processes) 3th, Roy D. Yates(作), 賴玲瑩(譯), 中文
2. 當代機率：理論與應用 4/e Ghahramani(作), 朱蘊鑛(譯), 中文
1. Probability and Stochastic Processes 3th, Roy D. Yates (made), 李白白白 (translation), Chinese
2. Contemporary Opportunity: Theory and Application 4/e Ghahramani (Written), Zhucai (Translation), Chinese
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 作業作業 Action |
40 | |
| 期中期中 Midterm |
30 | |
| 期末期末 End of the period |
30 |
