Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
To prepare students for the basic skills of model-based data analysis and R language.
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3.Professional Knowledge in Statistical Analysis |
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Written Presentation |
Assignment |
Attendance |
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Course Content and Homework/Schedule/Tests Schedule |
Week |
Course Content |
Week 1 |
Getting started with R
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Week 2 |
More tools in R |
Week 3 |
Multivariate normal distribution |
Week 4 |
Linear regression I |
Week 5 |
Linear regression II |
Week 6 |
GCV, AIC and best subset regression |
Week 7 |
Stepwise regression |
Week 8 |
L2-norm regularized (ridge) regression |
Week 9 |
Adaptive weighted ridge regression |
Week 10 |
Nonlinear regression: Regression tree |
Week 11 |
Bootstrap aggregation and random forest regression |
Week 12 |
K-fold cross-validation (CV) |
Week 13 |
Boosted trees |
Week 14 |
Implementing regressions with caret |
Week 15 |
Final report |
Week 16 |
Final report
Self-learning (LASSO regression)
Self-learning (overview of regressions) |
self-directed learning |
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Evaluation |
Homework assignments and class attendance watching lecture videos: 45%
Report: 45%
Self-learning: 10%
The grade system is tentative and subject to modification.
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Textbook & other References |
The elements of statistical learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman |
Teaching Aids & Teacher's Website |
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Office Hours |
1:00-2:00 PM Friday
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Sustainable Development Goals, SDGs(Link URL) |
04.Quality Education | include experience courses:N |
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