NCHU Course Outline

Course Name	(中) 數據分析與機器學習(5154)
	(Eng.) Data Analysis and Machine Learning
Offering Dept	Department of Mechanical Engineering
Course Type	Elective	Credits	3	Teacher	Bluest Lan
Department	Department of Mechanical Engineering/Graduate	Language	中/英文	Semester	2026-FALL
Course Description	Machine learning is bound up with artificial intelligence and its applications. This course provides an overview of the basic concepts related to data analysis, aiming at developing essential machine learning and data science skills.
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
At the conclusion of this subject students should be able to: 1. Describe the concepts of machine learning algorithms 2. Analyse and select appropriate approaches for real problems	1.The ability to solve engineering problems independently with professional knowledge in mechanical engineering. 2.The ability to think innovatively, plan and conduct research projects and to present research outcome in a professional format. 3.The ability to manage multi-disciplinary teams and to integrate cross-field technologies.. 4.A broader view of international competition/co-operation of industry. 5.The ability to lead, to manage and to plan life-long learning. 6.The knowledge of professional ethics and social responsibilities of a mechanical engineer.	20 20 15 15 15 15	Visit topic Discussion/Production Discussion Practicum Lecturing	Attendance Oral Presentation Study Outcome Quiz Written Presentation
Course Content and Homework/Schedule/Tests Schedule
Week Course Content Week 1 Introduction to Data Analysis and Machine Learning Course overview, supervised vs unsupervised Week 2 Linear Regression Prediction, evaluation, overfitting Week 3 Linear and Kernel-Based Classification Logistic regression, kNN, support vector machines Week 4 Model Evaluation and Regularisation Bias–variance, cross-validation, ridge and lasso Week 5 Decision Trees and Ensembles Splitting, random forests, feature importance Week 6 Clustering Unsupervised grouping, distance measures, evaluating cluster quality Week 7 Dimensionality Reduction PCA, variance, visualisation Week 8 Neural Networks Perceptrons, activation, backpropagation Week 9 Natural Language Processing Tokenisation, bag-of-words, text classification Week 10 Exam Covers Weeks 1–9: concepts, algorithms, and ML implementation Week 11 Project Development I Begin implementation; supervised coding sessions Week 12 Project Development II Continue development; checkpoints and mentoring Week 13 Project Development III Refinement and testing; peer evaluation Week 14 Project Development IV Short updates, debugging support, feedback sessions Week 15 Final Presentation I Formal presentations and oral defence Week 16 Final Presentation II Remaining presentations, peer review, reflection self-directed learning    03.Preparing presentations or reports related to industry and academia.
Evaluation
Class Participation (30%); Final Exam (40%); Final Project (30%)
Textbook & other References
Zaki, Data Mining and Machine Learning 2e. Cambridge University Press.
Teaching Aids & Teacher's Website
iLearning
Office Hours
After each lecture
Sustainable Development Goals, SDGs(Link URL)
include experience courses：N

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Update Date, year/month/day：2026/06/18 11:00:41	Printed Date, year/month/day：2026 / 8 / 18
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