The course will introduce fundamental concepts in statistical learning.
The emphasis will combine methodology with theoretical foundations and
computational aspects,
including
Chapter 1: Introduction
Chapter 2: Linear Regression
Chapter 3: Classification
Chapter 4: Basis Expansions and Nonlinear Regression
Chapter 5: Variable Selection and Regularization
Chapter 6: Tree-based Methods, Bagging, and Random Forest
Chapter 7: Boosting
Chapter 8: Some special topics
Optional Textbooks:
1.
Bertsekas, D. ``Convex Optimization Algorithms", Athena Scientific, 2015.
2.
Hastie, T., Tibshirani, R. and Wainwright, M. ``Statistical Learning with Sparsity:
The Lasso and Generalizations",Chapman and Hall/CRC, 2015.
3.
James, G., Witten, D., Hastie, T. and Tibshirani, R. ``An Introduction to
Statistical Learning with Applications in R", Springer, 2013.
4.
Hastie, T., Tibshirani, R. and Friedman, J. ``The Elements of Statistical Learning:
Data Mining, Inference, and Prediction, Second Edition", Springer, 2009.
Grading Policy
Your grade will be determined by homework (50 \%) and a final project (or a final
exam) (50 \%). No Late Homework Accepted!
生成式人工智慧倫理聲明：禁止使用
經仔細考量後，本課程授課教師認為不宜於此門課程當中使用生成式人工智慧於課堂學習當中。因本課
程的內容於生成式AI中尚有諸多錯誤，且容易影響學生對基礎核心知識之判讀。
根據本校公布之佈的「大學教育場域AI協作、共學與素養培養指引」，本門課程採取禁止使用，以下為
相關的監管機制
修讀本門課程之學生應注意本門課不得繳交使用生成式人工智慧所產出的作業、報告或個人心得。若經
查核發現，教師、學校或相關單位有權重新針對作業或報告重新評分或不予計分。
修讀本課程之學生於選課時視為同意以上倫理聲明。