NCHU Course Outline

Course Name	(中) 機器學習(4117)
	(Eng.) Machine Learning
Offering Dept	Department of Computer Science and Engineering
Course Type	Elective	Credits	3	Teacher	YANG CHING MING
Department	Department of Computer Science and Engineering/Undergraduate	Language	English	Semester	2025-FALL
Course Description	This course offers a comprehensive introduction to machine learning. Students will understand the theoretical foundations of machine learning algorithms and gain hands-on experience implementing them using Python and TensorFlow. Through real-world applications, students will demonstrate their ability to apply machine learning algorithms to solve practical problems.
Prerequisites				self-directed learning in the course	Y

Relevance of Course Objectives and Core Learning Outcomes(%)			Teaching and Assessment Methods for Course Objectives
Course Objectives	Competency Indicators	Ratio(%)	Teaching Methods	Assessment Methods
1. Understand the theoretical foundations of machine learning algorithms. 2. Implement machine learning algorithms using Python and TensorFlow. 3. Apply machine learning algorithms to real-world problems.	1.Having abilities on computer science literacy, information theory, and mathematical analysis.	100	Exercises Discussion Lecturing topic Discussion/Production	Written Presentation Attendance Assignment Quiz
Course Content and Homework/Schedule/Tests Schedule
Week Course Content Week 1 What is Machine Learning Week 2 End-to-End Machine Learning Task Week 3 Classification Week 4 Regression Week 5 Gradient Descent Week 6 Support Vector Machine Week 7 Decision Tree Week 8 Midterm Exam Week 9 Ensemble Learning and Random Forest Week 10 Data Preprocessing Week 11 Dimensionality Reduction Week 12 Unsupervised Learning Week 13 Deep Learning and Neural Network Week 14 Deep Learning and Neural Network Week 15 Reinforcement Learning Week 16 Final Exam self-directed learning    02.Viewing multimedia materials related to industry and academia. Reading ML-related papers
Evaluation
Midterm exam (30%) Final exam (30%) In-class exercises (10%) Quiz(10%) Self-directed learning (10%) Attendance (10%)
Textbook & other References
Self-made materials Reference books：Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems Authors：Aurélien Géron Publisher：O’REILLY
Teaching Aids & Teacher's Website
Self-made materials
Office Hours
Please make an appointment by email
Sustainable Development Goals, SDGs(Link URL)
04.Quality Education   08.Decent Work and Economic Growth   09.Industry, Innovation and Infrastructure   11.Sustainable Cities and Communities include experience courses：N

Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day：2025/08/07 13:45:49	Printed Date, year/month/day：2025 / 12 / 20
The second-hand book website：http://www.myub.com.tw/