國立中山大學 110學年度第1學期 課程教學大綱

National Sun Yat-sen University 110Academic year1st Semester Course syllabus

Course name(Chinese)


Course Code


Course name(English)


Type of the course










因應嚴重特殊傳染性肺炎(武漢肺炎),倘若後續需實施遠距授課,授課方式調整如下:Since COVID-19, if distance learning is necessary, the teaching methods would adjust as follows:

         同步遠距【透過網路直播技術,同時進行線上教學,得採Microsoft Teams、Adobe connect等軟體進行】
同步遠距含錄影【透過網路直播技術,同時進行線上教學並同時錄影,課程內容可擇日再重播,得採Microsoft Teams、Adobe connect等軟體進行】


因應嚴重特殊傳染性肺炎(武漢肺炎),倘若後續需實施遠距授課,評分方式調整如下:Since COVID-19, if distance learning is necessary, the evaluation would adjust as follows:


課程大綱 Course syllabus

         本課程教學大綱已提供完整英文資訊(本選項僅供統計使用,未提供完整英文資訊者,得免勾記)【Provide information of course syllabus in English.(This is for statistical use only. For those who do not provide information of course syllabus in English, do not check this field.)】

*** 無法存取網大課程頁面的同學請至: https://tinyurl.com/mis572cm503 輸入你的姓名學號。將統一由助教新增帳號。***
***第一周(9/29)上課將採 Google Meet 進行同步遠距課程, 請於此連結進入 https://meet.google.com/fkp-qvhx-dau ***
*** 本課程採「同步遠距含錄影」上課,課程與連線相關資訊將置於本校網路大學課程頁面, 請至本校圖資處首頁網路大學登入***
*** 本課程與 「CM 503- 大數據分析、機器學習、與人工智慧方法」併班上課 ***
Students will be introduced to theories & techniques in different fields related to AI, including probability & statistics, scientific computing, data engineering, algorithm design, statistical learning, deep learning, and explainable AI.
Basic understanding of relational databases, SQL, data structures, probability & statistics, college-level calculus, and matrix operations are required. Familiar with at least one high-level programming language. Scientific programming language, such as R, Matlab, Python, Scala, Julia, and SAS are preferred. Students who have taken CM502 are welcomed.

課程目標 Objectives

         This course is designed for students who need hands-on training of data engineering, machine learning, and general understanding of AI. We will begin with introduction to the rise and evolution of the Data Science. Its applications in various fields, such as business, healthcare, insurance, and finance, will also be discussed. This course covers the basic concepts of Big Data Analytics, Data Engineering, Business Analytics, MapReduce Design Patterns, Supervised & Unsupervised Machine Learning, in-memory computing, Functional Programming, Deep Neural Networks, and Machine Learning Interpretability. Students will also be introduced to how to use R & Python programming language and its packages to solve real-world big data problems.

授課方式 Teaching methods

         1. Onsite & online video lectures
2. In-class quiz

評分方式﹝評分標準及比例﹞Evaluation (Criteria and ratio)等第制單科成績對照表 letter grading reference

2.In-class exercise20%
4.Midterm Proposal20%
5.Final Project20%

參考書/教科書/閱讀文獻 Reference book/ textbook/ documents
No copies for intellectual property rights. Textbooks provided by the instructor used only for self-study, can not broadcast or commercial use

         * F. Provost, T. Fawcett. Data Science for Business: What you need to know about data mining and data-analytic thinking, O'Reilly Media, Inc., 2013.
* K. Hwang and M. Chen, Big-Data Analytics for Cloud, IoT and Cognitive Computing, 1st ed. Wiley Publishing, 2017.
* EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. John Wiley & Sons, 2015.
* N. Matloff, The Art of R Programming: A Tour of Statistical Software Design, 1st edition. No Starch Press, 2011.
* Kabacoff, Robert. R in Action. Manning Publications Co., 2011
* C. O'Neil and R. Schutt, Doing Data Science: Straight Talk from the Frontline, 1st edition. O'Reilly Media, 2013.
* G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: with Applications in R, 2nd Edition, Springer
(Available free online: https://www.statlearning.com/)
* F. Chollet and J. J. Allaire, Deep Learning with R, 1 edition. Manning Publications, 2018.
* A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Inc., 2019.
* W. McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O’Reilly Media, Inc., 2017.

每週課程內容及預計進度 Weekly scheduled progress

WeekDateContent and topic
12021/09/19~2021/09/25No Class
22021/09/26~2021/10/02Course Introduction
32021/10/03~2021/10/09Introduction to Data Engineering — I
42021/10/10~2021/10/16Introduction to Data Engineering — II
52021/10/17~2021/10/23Fundamentals of Data Analytics — I
62021/10/24~2021/10/30Fundamentals of Data Analytics — II
72021/10/31~2021/11/06Fundamentals of Data Analytics — III
82021/11/07~2021/11/13Introduction to Statistical Learning — I
92021/11/14~2021/11/20Project Proposal Defense – I
102021/11/21~2021/11/27Project Proposal Defense – II
112021/11/28~2021/12/04Introduction to Statistical Learning — II
122021/12/05~2021/12/11Supervised Learning — Regression
132021/12/12~2021/12/18Supervised Learning — Classification
142021/12/19~2021/12/25Topics in Interpretable Machine Learning
152021/12/26~2022/01/01Topics in Unsupervised Learning
162022/01/02~2022/01/08Topics in Model Architecture Design
172022/01/09~2022/01/15Term Project Presentation — I
182022/01/16~2022/01/22Term Project Presentation — II

課業討論時間 Office hours

         時段1 Time period 1:
時間 Time:星期一10am-12pm
地點 Office/Laboratory:By appointment
時段2 Time period 2:
時間 Time:星期三10am-12pm
地點 Office/Laboratory:By appointment

系所學生專業能力/全校學生基本素養與核心能力 basic disciplines and core capabilitics of the dcpartment and the university

basic disciplines and core capabilities of the department and the university
Class activities and evaluation
本課程欲培養之能力與素養 This course enables students to achieve.
紙筆考試或測驗 Test.
課堂討論︵含個案討論︶ Group discussion (case analysis).個人書面報告、作業、作品、實驗 Indivisual paper report/ assignment/ work or experiment.
群組書面報告、作業、作品、實驗 Group paper report/ assignment/ work or experiment.個人口頭報告 Indivisual oral presentation.群組口頭報告 Group oral presentation.課程規劃之校外參訪及實習 Off-campus visit and intership.證照/檢定 License.參與課程規劃之校內外活動及競賽 Participate in off-campus/ on-campus activities and competitions.課外閱讀 Outside reading.
※系所學生專業能力 Basic disciplines and core capabilities of the department
※全校學生基本素養與核心能力 Basic disciplines and core capabilities of the university
1.表達與溝通能力。 1. Articulation and communication skills           
2.探究與批判思考能力。 2. Inquisitive and critical thinking abilities           
3.終身學習能力。 3. Lifelong learning           
4.倫理與社會責任。 4. Ethnics and social responsibility           
5.美感品味。 5. Aesthetic appreciation           
6.創造力。 6. Creativity           
7.全球視野。 7. Global perspective           
8.合作與領導能力。 8. Team work and leadership           
9.山海胸襟與自然情懷。 9. Broad-mindedness and the embrace of nature            

本課程與SDGs相關項目:The course relates to SDGs items:

SDG1-消除貧窮(No Poverty)
SDG2-消除飢餓 (Zero Hunger)
SDG3-良好健康與福祉(Good Health and Well-being)
SDG4-教育品質(Quality Education)
SDG5-性別平等(Gender Equality)
SDG6-乾淨水源與公共衛生(Clean Water and Sanitation)
SDG7-可負擔乾淨能源(Affordable and Clean Energy)
SDG8-優質工作與經濟成長(Decent Work and Economic Growth)
SDG9-工業、創新和基礎建設(Industry,Innovation and Infrastructure)
SDG10-減少不平等(Reduced Inequalities)
SDG11-永續城市(Sustainable Cities and Communities)
SDG12-責任消費與生產(Responsible Consumption and Production)
SDG13-氣候行動(Climate Action)
SDG14-海洋生態(Life Below Water)
SDG15-陸域生態(Life on Land)
SDG16-和平、正義和穩健的制度(Peace,Justice And Strong Institutions)
SDG17-促進目標實現的全球夥伴關係(Partnership for the Goals)

本課程校外實習資訊: This course is relevant to internship: