| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| 培養具備人工智慧專業知識與應用能力兼備之數據分析人才。 |
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| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
R language and data science introduction
Regression introduction
Simple linear regression
Multiple linear regression
Model adequacy checking
Transformations and weighting to correct model inadequacies Diagnostics for leverage and influence
Polynomial regression models
Indicator variables
Multicollinearity
Variable selection and model building
Validation of regression models
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| Week 2 |
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| Week 3 |
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| Week 4 |
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| Week 5 |
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| Week 6 |
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| Week 7 |
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| Week 8 |
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| Week 9 |
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| Week 10 |
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| Week 11 |
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| Week 12 |
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| Week 13 |
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| Week 14 |
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| Week 15 |
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| Week 16 |
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self-directed
learning |
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| Evaluation |
Midterm Examination 20%
Final examination (or presentation) 20%
Class participation, Quizzes, homework, and others 60%
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| Textbook & other References |
「Introduction to Linear Regression Analysis, 5th Edition」，by Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining
「Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow 2/e」，by Aurélien Géron, 2019
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| Teaching Aids & Teacher's Website |
| https://www.youtube.com/channel/UCSivAooQ-OTLATS1dTT3DZw |
| Office Hours |
| Appointment |
| Sustainable Development Goals, SDGs(Link URL) |
| | include experience courses：N |
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