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Chapter 6: Nonlinear methods (2 pages, Times New Roman, 12-point font size)
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• Summarize polynomial regression, regression with step functions, regression splines,
local regression, generalized additive model
• What are the drawbacks of polynomial regression?
• What are the drawbacks of regression with step functions?
• What are the differences between cubic spline and natural cubic spline? Compared
with cubic spline, what is the benefit of natural cubic spline?
• What methods do we use to find the regression coefficients in polynomial regression,
regression with step function, linear spline, and cubic spline?
• Are there any tuning parameters in the methods? How to tune these parameters?
Chapter 7: Tree-based methods (2.5 pages, Times New Roman, 12-point font size)
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• Summarize regression tree, classification tree, bagging, random forest, boosting for
regression tree, AdaBoost
• What is the key idea behind bagging?
• Why does random forest significantly improves over bagging?
• What are the differences between random forest and boosting?
• What are the key components in boosting for regression tree and AdaBoost?
• Are there any tuning parameters in the methods that have been introduced? How to
tune these parameters?
Chapter 9: Unsupervised learning (2 pages, Times New Roman, 12-point font size)
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• Summarize K-means clustering, K-medoids clustering, and hierarchical clustering
• How to solve the objective function in K-means clustering?
• What are the differences between K-means clustering and K-medoids clustering?
• What are the pros and cons of K-means clustering and K-medoids clustering?
• How to choose the number of clusters in K-means clustering?
