一、課程目標
1. 學習如何利用機器學習建立模型協助企業制定行銷決策。
2. 瞭解機器學習主要演算法的原理、使用目的與應用時機。
3. 針對行銷決策情境與內部數據資產，進行行銷大數據分析。
4. 解讀機器學習模型的有效性與產生行銷決策之洞察與意涵。
5. 學會操作SAS Viya進行機器學習之建模與分析結果解讀。
二、課程設計
　　本課程將學習如何運用自動化機器學習(Auto ML)之軟體建構機器學習模型以協助行銷決策。本課程將以教師課堂講授與示範方式，並由學生分組針對企業或市場上特定行銷議題進行資料蒐集或現有公開數據資料之擷取，利用SAS Viya進行建模分析，實際演練與應用本課程講授之分析方法。教學方法概述如下：
1. 教師課堂講授→由教師在課堂上針對大數據分析如何協助行銷決策、及機器學習（ML）主要演算法的基本概念、分析情境、及分析結果的解讀與應用，進行講授。
2. SAS Viya操作→由教師帶領學生在電腦上實際操作SAS Viya 中主要ML模型的建置與分析報表解讀。
3. 智慧行銷專題之實做→針對小組想探討的行銷問題，透過擷取市場數據資料庫或公開的數據資料進行建模與分析，產生關鍵資訊或情報，提出行銷決策之建議，並撰寫書面分析報告及進行口頭簡報。

1. Course Objectives
1. Learn how to use machine learning to build models to assist companies in making marketing decisions.
2. Understand the principles, purpose and timing of application of the main algorithms of machine learning.
3. Conduct marketing big data analysis based on marketing decision-making situations and internal data assets.
4. Interpret the effectiveness of machine learning models and the insights and implications of generating marketing decisions.
5. Learn to operate SAS Viya for machine learning modeling and interpretation of analysis results.
2. Course design
This course will learn how to use automated machine learning (Auto ML) software to construct machine learning models to assist in marketing decisions. This course will be taught and demonstrated by teachers, and students will be divided into groups to collect data or extract existing public data on specific marketing issues in the company or market, use SAS Viya to conduct modeling analysis, and actually practice and apply the analysis methods taught in this course. An overview of teaching methods is as follows:
1. Teacher’s classroom lecture → The teacher will teach in class how big data analysis can assist marketing decisions, as well as the basic concepts of the main algorithms of machine learning (ML), analysis scenarios, and the interpretation and application of analysis results.
2. SAS Viya operation → Teachers will lead students to actually operate the construction of main ML models and analysis report interpretation in SAS Viya on the computer.
3. Implementation of the smart marketing topic → Based on the marketing issues that the group wants to discuss, conduct modeling and analysis by extracting market data databases or public data, generate key information or intelligence, propose marketing decision-making suggestions, and write written analysis reports and give oral presentations.

以自編教材為主
● 參考教材
1. SAS Institute (2023). Machine Learning Using SAS Viya Course Notes. North Carolina: SAS.
2.山口達輝、松田洋之 著，衛宮紘 譯（2020）。圖解AI：機器學習和深度學習的技術與原理。碁峰出版。
● SAS Viya操作影片
SAS官網部落格：Viya step by step操作教學。https://blogs.sas.com/content/sastaiwan/sas-viya/viya-線上影音教學/
● 其他參考資料
Kaggle數據建模和數據分析競賽平台：https://www.kaggle.com/

Mainly self-compiled teaching materials
● Reference materials
1. SAS Institute (2023). Machine Learning Using SAS Viya Course Notes. North Carolina: SAS.
2. Written by Yamaguchi Tatsuki and Matsuda Hiroyuki, translated by Emiya Hong (2020). Illustrated AI: Technologies and principles of machine learning and deep learning. Published by Qi Feng.
● SAS Viya operation video
SAS official website blog: Viya step by step operation teaching. https://blogs.sas.com/content/sastaiwan/sas-viya/viya-online video teaching/
● Other reference materials
Kaggle data modeling and data analysis competition platform: https://www.kaggle.com/