東吳大學教師授課計劃表

檔案產生時間:2019/9/26 下午 03:17:03
本表如有異動,於4小時內自動更新
一、課程基本資料 Course Information
科目名稱 Course Title:
(中文)機器學習導論
(英文)INTRODUCTION TO MACHINE LEARNING WITH R
開課學期 Semester:108學年度第1學期
開課班級 Class:經三A
授課教師 Instructor:米克里斯多 MICHALOPOULOS, CHRISTOS
科目代碼 Course Code:BEC36901 單全學期 Semester/Year:單 分組組別 Section:
人數限制 Class Size:40 必選修別 Required/Elective:選 學分數 Credit(s):2
星期節次 Day/Session: 一56  前次異動時間 Time Last Edited:108年06月20日00時07分
經濟學系基本能力指標 Basic Ability Index
編號
Code
指標名稱
Basic Ability Index
本科目對應之指標
Correspondent Index
達成該項基本能力之考評方式
Methods Of Evaluating This Ability
1具備經濟學核心知識
Core economic knowledge.
  
2具備經濟應用及政策分析能力
The ability to apply economic theories and conduct policy analysis.
》出缺席狀況
》課堂討論與表現
》資料蒐集與分析
》外文閱讀
3具備邏輯思考能力
The ability of logical thinking.
》出缺席狀況
》課堂討論與表現
》報告(含個人或小組、口頭或書面、專題、訪問、觀察等形式)
》作業成績
》資料蒐集與分析
》外文閱讀
4具備數理分析能力
The ability to perform mathematical analysis.
》出缺席狀況
》課堂討論與表現
》報告(含個人或小組、口頭或書面、專題、訪問、觀察等形式)
》作業成績
》外文閱讀
5具備統計分析能力
The ability to perform statistical analysis.
》出缺席狀況
》課堂討論與表現
》報告(含個人或小組、口頭或書面、專題、訪問、觀察等形式)
》資料蒐集與分析
》外文閱讀
6具備金融與財務專業能力
Professional skills in money, banking and finance.
》出缺席狀況
》課堂討論與表現
》報告(含個人或小組、口頭或書面、專題、訪問、觀察等形式)
》作業成績
》資料蒐集與分析
》外文閱讀
7具備資料收集及表達能力
The ability to gather information and to make presentations.
》出缺席狀況
》課堂討論與表現
》資料蒐集與分析
》外文閱讀
》團隊參與
》學習綜合表現
8具備英文閱讀能力
The ability to read English proficiently.
》出缺席狀況
》課堂討論與表現
》資料蒐集與分析
》外文閱讀
》學習綜合表現
二、指定教科書及參考資料 Textbooks and Reference
(請修課同學遵守智慧財產權,不得非法影印)
●指定教科書 Required Texts
My lecture notes. No need to buy a textbook!
●參考書資料暨網路資源 Reference Books and Online Resources
I will give a list in the class.
三、教學目標 Objectives
In this class, students will learn the basics of machine learning using R (free software). They will learn data mining and machine learning techniques which help discover previously unknown patterns and relationships in data. Machine learning is a collection of sophisticated mathematical algorithms used to segment the data and predict the likelihood of future events based on past events. In this class, many such algorithms will be explained simply and with many examples with real data for students to learn how to use such techniques on their own.
After this class, students will be confident enough to use the tools learned and even go deeper by reading more mathematically oriented textbooks on machine learning. They will gain important experience in analyzing many types of data using a variety of statistical techniques and algorithms that might be valuable for his/her future employment as a data analyst in any type of industry.
In this class, students will learn the basics of machine learning using R (free software). They will learn data mining and machine learning techniques which help discover previously unknown patterns and relationships in data. Machine learning is a collection of sophisticated mathematical algorithms used to segment the data and predict the likelihood of future events based on past events. In this class, many such algorithms will be explained simply and with many examples with real data for students to learn how to use such techniques on their own.
After this class, students will be confident enough to use the tools learned and even go deeper by reading more mathematically oriented textbooks on machine learning. They will gain important experience in analyzing many types of data using a variety of statistical techniques and algorithms that might be valuable for his/her future employment as a data analyst in any type of industry.
四、課程內容 Course Description
整體敘述 Overall Description
●分週敘述 Weekly Schedule
週次 Wk 日期 Date 課程內容 Content 備註 Note

1

9/9 Introducing Machine Learning   

2

9/16 Managing and Understanding Data with R   

3

9/23 Managing and Understanding Data with R   

4

9/30 Basic Classification Using Nearest Neighbors   

5

10/7 Basic Classification Using Nearest Neighbors   

6

10/14 Basic Classification Using Naive Bayes   

7

10/21 Basic Classification Using Naive Bayes   

8

10/28 Classification Using Decision Trees and Rules   

9

11/4 Classification Using Decision Trees and Rules   

10

11/11 Regression Methods (Linear regression, Ridge Regression, Lasso Regression)   

11

11/18 Regression Methods (Linear regression, Ridge Regression, Lasso Regression)   

12

11/25 Regression Methods (Linear regression, Ridge Regression, Lasso Regression)   

13

12/2 Regression Methods (Linear regression, Ridge Regression, Lasso Regression)   

14

12/9 Neural Networks and Support Vector Machines   

15

12/16 Neural Networks and Support Vector Machines   

16

12/23 Further Topics (Boosting, k-means clustering, Random forests).   

17

12/30 Further Topics (Boosting, k-means clustering, Random forests).   

18

1/6 Project-Presentation.   
五、考評及成績核算方式 Grading
配分項目 Items 次數 Times 配分比率 Percentage 配分標準說明 Grading Description
出席1720% 
平時作業530% 
分組作業150% 
配分比率加總 100%  
六、授課教師課業輔導時間和聯絡方式 Office Hours And Contact Info
●課業輔導時間 Office Hour
課堂告知
●聯絡方式 Contact Info
研究室地點 Office:3413 EMAIL:
聯絡電話 Tel: 其他 Others:
七、教學助理聯絡方式 TA’s Contact Info
教學助理姓名 Name 連絡電話 Tel EMAIL 其他 Others
八、建議先修課程 Suggested Prerequisite Course
no
九、課程其他要求 Other Requirements
no
十、學校教材上網及教師個人網址 University’s Web Portal And Teacher's Website
學校教材上網網址 University’s Teaching Material Portal:
東吳大學Moodle數位平台:http://isee.scu.edu.tw
教師個人網址 Teacher's Website:
其他 Others:
十一、計畫表公布後異動說明 Changes Made After Posting Syllabus