課程大綱

課程資訊

課程名稱

數據科學與決策科技
Data Science and Decision Technology

開課學期

109-2

授課對象

工學院化學工程學研究所

授課教師

張良志

課號

ChemE5070

課程識別碼

524EU2280

班次

學分

3.0

全/半年

半年

必/選修

選修

上課時間

星期二6,7,8(13:20~16:20)

上課地點

新502

備註

本課程以英語授課。化工選修課程。
總人數上限：40人

課程簡介影片

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課程概述

This course consists of two parts. The first part gives a broad coverage of modern modeling, Data Science, Machine Learning (ML) methods used for prediction and classification. It also includes Deep Learning Neural Networks for image, language, and time-series data processing. This is the foundation of today's AI applications. The second part provides a solid foundation of optimal decision making that can be applied to multiple business and engineering disciplines such as planning, profit maximization, business strategy setting, and logistics. Business and engineering applications will be discussed throughout the course.

課程目標

Provides students with the theoretical foundation and hands-on practice of data science and optimal decision making.
After the course students should have a good understanding of major machine learning algorithms and the common modeling issues. They should also understand how to formulate a business issue as an optimization problem and how to reach the optimal decision. The skills can be applied to their engineering, business, or data scientist careers.

課程要求

College-level math, Excel, Matlab Learners (prior Matlab experience is not necessary). It's acceptable if a student choose to use Python to do homework.

預期每週課後學習時數

Office Hours

指定閱讀

1. "Hands-on Machine Learning with Scikit-Learn & TensorFlow", 2nd, A. Geron, O'Reilly, 2019.
2. "Management Science", 4th ed, S.G. Powell and K.R. Baker, Wiley, 2014.
Optional (Nice to Have)

參考書目

“Deep Learning using Python”, F. Chollet, Manning, 2018
“Practical Statistics for Data Scientists”, P. Bruce and A. Bruce, O’Reilly, 2017
Intel Nirvana AI Academy, 2019
“Predictive Analytics”, E. Siegel, Wiley, 2017
Coursera: Deep Learning Specialization, Andrew Ng, 2019.
Machine Learning, Stanford University thru Coursera, Andrew Ng
“Deep Learning Illustrated”, J. Krohn et.al, Addison-Wesley, 2020.
“Data Science for Business”, F Provost and T. Fawcett, O’Reilly, 2013
“Machine Learning with Matlab”, J. Smith, ?
“An Introduction to Statistical Learning”, 7th ed, G. James, D. Witten, T, Hastie, R. Tibshirani, Springer, 2017. (note: use R in examples)

評量方式
(僅供參考)

No.	項目	百分比	說明
1.	Midterm	30%
2.	Final	30%
3.	Class Interaction	5%
4.	Homework	35%

課程進度

週次	日期	單元主題
第1週	2/23	Course introduction and policy. State of art of Machine Learning. Machine learning landscape and examples.
第2週	3/02	Introduction to optimization (Part I). Linear Regression and common modeling issues.
第3週	3/09	Common modeling issues e.g. feature engineering, data preprocessing, overfit and underfit etc..
第4週	3/16	Classification methods-I and business applications (Logistic Regression),Principal Component Analysis (PCA) and its applications
第5週	3/23	Classification methods (KNN, Logistic Regression, Decision Tree, Support Vector Machine)
第6週	3/30	Classification Performance measures and Clustering , Unsupervised learning
第7週	4/06	Neural Network structures and calculation. Neural Net training
第8週	4/13	Deep Learning Neural Net I (Convolutional NN)
第9週	4/20	Deep Learning Neural Net II (Recurrent NN for sequence data)
第10週	4/27	Mid-term Exam
第11週	5/04	Applications of Machine Learning in Chem Eng. Mathematical foundation of constrained optimization
第12週	5/11	Linear Programming I: Translating business problem into LP formulation.
第13週	5/18	Linear Programming II Network Model, Applications in Model Predictive Control.
第14週	5/25	Non-Linear Programming in Engineering applications
第15週	6/01	Real-time Optimization in refinery and chemical processes. Integer Programming
第16週	6/08	Integer Programming and Mixed Integer Programming
第17週	6/15	Uncertainty and Monte-Carlo Simulation. Review for Final Exam