國立中興大學教學大綱

課程名稱	(中) 數據科學方法(5139)
	(Eng.) Fundamental Methodology for Data Science
開課單位	資科所
課程類別	選修	學分	3	授課教師	蔡鴻旭
選課單位	應數系 / 學士班	授課使用語言	中文	英文/EMI		開課學期	1141
課程簡述	This Data Science Course is introductory to Data Science. The syllabus of the course will introduce students to methods of processing data before dealing data, and concepts of data science algorithms for machine learning that help to gain some meaningful insights from structure or unstructured data. Statistics courses just analyze the history of the data, but with the help of data science courses and machine learning algorithms, students can predict future trends (profit, loss, or other insights). Therefore, the prediction results help students who want to learn how to make data-driven decisions and analyze ways to maximize the profit of an organization using data. The course will introduce the machine learning and/or deep learning algorithms for artificial intelligent approaches of learning from data.
先修課程名稱

課程與核心能力關聯配比(%)

課程目標之教學方法與評量方法

課程目標

核心能力

配比(%)

教學方法

評量方法

Let students understand basic math background for data science.
Let students understand approaches of learning from data.
Let students understand the trend on the development of data science.
Let students develop data models for real problems.

1.數理基礎知識

4.計算科學專業知識

專題探討/製作

討論

講授

書面報告

出席狀況

口頭報告

作業

實作

授課內容(單元名稱與內容、習作/每週授課、考試進度-共16週加自主學習)

週次	授課內容
第1週	Week #1: Introduction of syllabus, Data Science, building programming environment for data science, Computer assignment, Computer Project, Journal paper study and presentation, Aidea, https://aidata.nchu.edu.tw 1~2 classmates for each team Introduction of Artificial Intelligence and Big Data Data and data preparation
第2週	Week #2: Introduction of machine learning and its applications Applications of Machine Learning Association rule Naïve Bayes classifier Logistic Regression Decision Trees Random Forests K Nearest Neighbors algorithm Dimensionality reduction Clustering
第3週	Week #3-#4: Introduction to Neural network and its application (please refer to the URL, https://airobot.ccu.edu.tw/chapter-2-從感知網路說起/) Introduction to Neural network Architecture of Perceptrons Multi-layers architecture of Perceptrons Matrix computation for Perceptrons Recognition for Perceptron Network Learning algorithm for Perceptron Network Application on artificial Neural network - Handwriting image recognition
第4週	Week #3-#4: Introduction to Neural network and its application Perceptron Multi-layers Perceptron Support Vector Machine Support Vector Regression Back-propagation(BP) Gradient-vanishing Regulation
第5週	Week #5:Deep Learning- using Convolutional Neural Network (CNN) on the design of classifications and practical exercises) Introduction to deep learning Deep learning for CNN Practical exercises for CNN Using Keras CNN on recognition for MNIST handwritten digit database Keras Cifar-10 image database Using Keras CNN on recognition for Cifar-10 image database
第6週	Week #6: Models based on CNN LeNet VGGNet Residual Network DenseNet U-Net InceptionNet(GoogLeNet) Fully Convolutional Networks(FCNs) MobileNet V1 EfficientNet MaskRCNN Capnet VAE GAN
第7週	Week #07-#09: midterm report Computer project #1 report and presentation for each team Draft of project report with word and presentation by ppt for each team Present 1st paper by ppt for each student
第8週	Week #07-#09: midterm report Computer project #1 report and presentation for each team Draft of project report with word and presentation by ppt for each team Present 1st paper by ppt for each student
第9週	Week #07-#09: midterm report Computer project #1 report and presentation for each team Draft of project report with word and presentation by ppt for each team Present 1st paper by ppt for each student
第10週	Week #10:CNN applications for image classification, image retrieval and image segmentation Autoencoder and decorder (image transformer) Transfer Learning You Only Look Once(YoLo) RCNN/Fast-RCNN Multi-label/Multiclass/Multilabel Multitask CNNs are applied in the design of image classification, image retrieval, and image segmentation
第11週	Week #11: paper survey for CNN applications -Content-Based Image Retrieval Based on CNN and SVM --VAE-Y-Autoencoders-disentangling latent representations via sequential-encoding -Neural network-based multi-task learning for inpatient flow classification and length of stay prediction -DML-PL Deep metric learning based pseudo-labeling framework -Hard Sample Aware Noise Robust Learning
第12週	Week #12: CNN applications for Intelligent medical data analysis. -Cough sound -Lung sound (Respiratory Sound) -CXR images -CT images
第13週	Week #13: deep learning - Long short term memory model for the design of prediction models and practical exercises Deep Learning–RNN-LSTM recurrent neural networks(RNN) Long short-term memory (LSTM)
第14週	Week #14-#16: final-term report computer project #2 report with word format and presentation by ppt for each team Present 2nd paper by ppt for each student
第15週	Week #14-#16: final-term report Computer project #2 report with word format and presentation by ppt for each team Present 2nd paper by ppt for each student
第16週	Week #14-#16: final-term report Computer project #2 report with word format and presentation by ppt for each team Final project report with ”word” (NOT pdf) format and presentation by ppt for each team, which highlights (survey) at least 15 sci/ssci journal papers (NOT Mega or open access Journals) for the topic of the computer projects
自主學習內容	01.參與專業論壇、講座、企業分享等產官學研相關交流活動 03.製作專題報告 05.參與本校各單位舉辦之各類工作坊活動

學習評量方式

Participation: 10%; Paper presentation: 30%; Computer assignment and report: 20%; Project (Project proposal, result and report): 40%; Bonus: 20%

教科書＆參考書目(書名、作者、書局、代理商、說明)

==Textbook
大數據分析與資料挖礦 2/e，作者：簡禎富、許嘉裕，(前程文化)
Big-data-analytics-Data-mining 2/e, authors: Hsu, Chia-yu and Chien, Chen-fu (https://www.fcmc.com.tw/)
製造數據科學，作者：李家岩、洪佑鑫 (前程文化)
Data Science in Manufacturing, authors: Hung, Yu-Hsin Jeff and Lee, Chia-Yen (https://www.fcmc.com.tw/)

===References==
Data Science from Scratch: First Principles with Python, 2/e (O’Reilly)
Data Science from Scratch｜用 Python 學資料科學, 2/e (中文版) (碁峰資訊)
https://github.com/joelgrus/data-science-from-scratch
======
Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.)
書刊名: 精通機器學習使用Scikit-Learn, Keras與TensorFlow，作者: 杰龍 (Géron, Aurélien)，其他作者: 賴屹民，(歐禮萊)
======
https://scikit-learn.org/stable/user_guide.html
===
Lau et al., Principles and Techniques of Data Science. https://ds100.org/sp18/assets/lectures/lec01/01-intro-to-data100_v2.pdf
==
Python資料科學與機器學習：從入門到實作必備攻略 (博碩文化)
=====
Raschka, Sebastian, and Vahid Mirjalili. Python Machine Learning, 3rd Ed. Packt Publishing, 2019.
https://github.com/rasbt/python-machine-learning-book-3rd-edition
Python機器學習第三版（上）譯者：劉立民、吳建華　譯(博碩文化)
Python機器學習第三版（下）譯者：劉立民、吳建華　譯(博碩文化)
====
資料科學的建模基礎 - 別急著coding！你知道模型的陷阱嗎？作者：江崎貴裕著、王心薇譯、施威銘研究室監修(旗標)
===
Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT
Neural Networks: A Comprehensive Foundation, Simon Haykin
https://airobot.ccu.edu.tw/chapter-2-%e5%be%9e%e6%84%9f%e7%9f%a5%e7%b6%b2%e8%b7%af%e8%aa%aa%e8%b5%b7/
==
深度學習-影像處理應用，彭彥璁、李偉華、陳彥蓉，全華圖書 (Deep learning for image processing applications), 2023.06.

課程教材（教師個人網址請列在本校內之網址）

http://www.amath.nchu.edu.tw/member_detail.php?Key=71

課程輔導時間

星期二
Tuesday 8th-9th classes

聯合國全球永續發展目標(連結網址)

04.教育品質

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更新日期西元年/月/日：2025/09/07 23:15:18	列印日期西元年/月/日：2025 / 9 / 18
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