Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2025 342109 Machine Learning 1 3.0 3 ★ You-Jin Park GIEM 20 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	342109	Machine Learning	1	3.0	3	★	You-Jin Park	GIEM	20	0

Course Plan：

Instructor You-Jin Park　Office Hours

Email eugenepark993@gmail.com

Last Updated 2024-12-18 13:38:48

Course Objective This course introduces students to the process of data science, covering problem formulation, data acquisition, modeling and analysis, and presentation and integration into action. Students will learn useful skills as well as basic statistical methods by completing several hands-on problems focused on real-world data. This course starts with a brief introduction to multivariate data analysis and data summarization including visualization. Then it extends to focus on using data to develop predictive capabilities using supervised and unsupervised machine learning models (regression, classification, clustering, PCA, etc.).

Course Schedule Week 1 Data summarization and visualization Week 2-4 Multivariate normal distribution Week 5-6 Principal component analysis Week 7-8 Naïve Bayes, Neural networks Week 9 Exam or assignment Week 10-14 Supervised learning (Regression, Classification, Decision tree, kNN, SVM, Ensemble method – Bagging, RF, Boosting) Week 15-17 Unsupervised learning (Clustering, k-means) Week 18 Exam or assignment

Evaluation and grading policy 1. Final Exam or Team Project: 40 % 2. Assignment: 50 % 3. Attendance: 10 %

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Textbook: ‘Applied Multivariate Statistical Analysis’ written by Richard A. Johnson and Dean W. Wichern. Supplementary references: 'Machine Learning (1997)' written by Tom Mitchell, ‘Introduction to Machine Learning with Python: A Guide for Data Scientists’ written by Andreas C. Müller and Sarah Guido

The access to curricular consultation Email or i-school system.
Office hour: Thursday 11:00 ~ 12:00

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI

Note
E-mail and MS Teams

Instructor	You-Jin Park　Office Hours
Email	eugenepark993@gmail.com
Last Updated	2024-12-18 13:38:48
Course Objective	This course introduces students to the process of data science, covering problem formulation, data acquisition, modeling and analysis, and presentation and integration into action. Students will learn useful skills as well as basic statistical methods by completing several hands-on problems focused on real-world data. This course starts with a brief introduction to multivariate data analysis and data summarization including visualization. Then it extends to focus on using data to develop predictive capabilities using supervised and unsupervised machine learning models (regression, classification, clustering, PCA, etc.).
Course Schedule	Week 1 Data summarization and visualization Week 2-4 Multivariate normal distribution Week 5-6 Principal component analysis Week 7-8 Naïve Bayes, Neural networks Week 9 Exam or assignment Week 10-14 Supervised learning (Regression, Classification, Decision tree, kNN, SVM, Ensemble method – Bagging, RF, Boosting) Week 15-17 Unsupervised learning (Clustering, k-means) Week 18 Exam or assignment
Evaluation and grading policy	1. Final Exam or Team Project: 40 % 2. Assignment: 50 % 3. Attendance: 10 %
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Textbook: ‘Applied Multivariate Statistical Analysis’ written by Richard A. Johnson and Dean W. Wichern. Supplementary references: 'Machine Learning (1997)' written by Tom Mitchell, ‘Introduction to Machine Learning with Python: A Guide for Data Scientists’ written by Andreas C. Müller and Sarah Guido
The access to curricular consultation	Email or i-school system. Office hour: Thursday 11:00 ~ 12:00
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI
Note	E-mail and MS Teams