國立中山大學 108學年度第1學期 課程教學大綱

National Sun Yat-sen University 108Academic year1st Semester Course syllabus

中文名稱
Course name(Chinese)

大數據分析、機器學習、與人工智慧方法

課號
Course Code

CM503

英文名稱
Course name(English)

BIG DATA ANALYTICS, MACHINE LEARNING, AND ARTIFICIAL INTELLIGENCE

課程類別
Type of the course

講授類

必選修
Required/Selected

選修

系所
Dept./faculty

管理學院

授課教師
Instructor

康藝晃    

學分
Credit

3

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:

         尚未建立傳染性肺炎(武漢肺炎)授課方式調整

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:

         尚未建立傳染性肺炎(武漢肺炎)課程評分方式﹝評分標準及比例﹞

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.)】

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.
Prerequisite:
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 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

        
1.In-class quiz20%
2.Homework30%
3.Midterm Proposal20%
4.Final Project30%

參考書/教科書/閱讀文獻 Reference book/ textbook/ documents
〔請遵守智慧財產權觀念,不可非法影印。教師所提供之教材供學生本人自修學習使用,不得散播及做為商業用途〕
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, Springer
(Available free online: http://www-bcf.usc.edu/~gareth/ISL/)
* F. Chollet and J. J. Allaire, Deep Learning with R, 1 edition. Manning Publications, 2018.

每週課程內容及預計進度 Weekly scheduled progress

        
週次日期授課內容及主題
WeekDateContent and topic
12019/09/09~2019/09/15Course Introduction
22019/09/16~2019/09/22Introduction to Data Engineering with R — I
32019/09/23~2019/09/29Introduction to Data Engineering with R — II
42019/09/30~2019/10/06Fundamentals of Data Analytics — I
52019/10/07~2019/10/13Fundamentals of Data Analytics — II
62019/10/14~2019/10/20Introduction to Machine Learning
72019/10/21~2019/10/27Supervised Learning — Regression
82019/10/28~2019/11/03Project Proposal Defense – I
92019/11/04~2019/11/10Project Proposal Defense – II
102019/11/11~2019/11/17Supervised Learning — Classification
112019/11/18~2019/11/24Supervised Learning — Tree-based Models
122019/11/25~2019/12/01Supervised Learning — Rule Learning & ML Interpretability
132019/12/02~2019/12/08Unsupervised Learning — Matrix Factorization Techniques
142019/12/09~2019/12/15Unsupervised Learning — Autoencoders
152019/12/16~2019/12/22Introduction to Deep Learning
162019/12/23~2019/12/29Term Project Presentation — I
172019/12/30~2020/01/05No Class (元旦假期)
182020/01/06~2020/01/12Term 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

        
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 capabilities of the department and the university
課堂活動與評量方式
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 skillsV          
2.探究與批判思考能力。 2. Inquisitive and critical thinking abilitiesV          
3.終身學習能力。 3. Lifelong learning           
4.倫理與社會責任。 4. Ethnics and social responsibilityV          
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 leadershipV          
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:

         尚未建立SDGS資料

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

         本課程無註記包含校外實習

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