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*** 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*** *** 未選上課無法登入的同學，第一堂課上課請至 https://meet.google.com/yhq-eada-jrs *** *** 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. 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.
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
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
* 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. Buisson, Behavioral Data Analysis with R and Python. O’Reilly Media, Inc., 2021. * 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. * 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. * 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. * C. Molnar, Interpretable Machine Learning: A Guide For Making Black Box Models Explainable. Munich, Germany: Independently published, 2022. (https://christophm.github.io/interpretable-ml-book/) * J. Pearl, M. Glymour, and N. P. Jewell, Causal Inference in Statistics - A Primer, 1st edition. Chichester, West Sussex: Wiley, 2016.
每週課程內容及預計進度 Weekly scheduled progress
Content and topic
Introduction to Data Engineering — I
Introduction to Data Engineering — II
Introduction to Data Engineering — III
Fundamentals of Data Analytics — I
Fundamentals of Data Analytics — II
Fundamentals of Data Analytics — III
Introduction to Statistical Learning — I
Project Proposal Defense — I
Project Proposal Defense — II
Introduction to Statistical Learning — II
Supervised Learning — Regression
Supervised Learning — Classification
Topics in Unsupervised Learning
Topics in Interpretable Machine Learning
Topics in Causal Inference
Term Project Presentation — I
Term 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.
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.
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
1.具備資訊倫理與社會責任實踐的能力 1. Awareness of information ethics.
2.具備溝通能力 2. Communication Skills.
3.具備解決資訊管理問題之能力 3. Capabilities to solve IT-related problems.
4.具備資訊管理之專業知識 4. Professional knowledge of information technology.
5.具備國際觀 5. Global perspective.
※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
Internship: The required or elective courses should include credits and learning hours. Students should participate in the corporative company or institution to practice and learn the real skills. An internship certification must be handed in at the end of internship to get the credits or to fulfil the graduation requirements.