課程資訊

Statistical Learning

111-1

STAT5009

250 U0090

3.0

Statistical Learning refers to a vast of statistical approaches for understanding data. These approaches can be roughly divided into two categories: supervised and unsupervised. In supervised learning, the goal is to predict the response variable; in unsupervised learning, the goal is to describe the associations and patterns among a set of variables. This course concentrates on the topics related to classification and clustering given that linear regression and non-parametric regression are covered in the courses Regression Analysis and Non-parametric Regression, respectively.

Those commonly employed approaches for classification and those for clustering will be introduced with proper examples. After taking the course, the students are expected to be able to utilize those approaches properly and perform sensible analysis.

Calculus, Linear Algebra, and Statistics

Office Hours

Hastie, T., Tibshirani, R. and Friedman, J. (2016). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition. Springer.

(僅供參考)

 上課形式 作業繳交方式 考試形式 其他 由師生雙方議定
 課程進度
 週次 日期 單元主題 第1週 9/7/2022 Introduction (supervised learning, unsupervised learning, statistical decision theory, function approximation, structured regression models and model selection) 第2週 9/14/2022 Linear methods for classification (linear regression, LDA, and QDA) 第3週 9/21/2022 Linear methods for classification (logistic regression, and separating hyperplanes) Basis Expansions and Regularization (piecewise polynomials and splines) 第4週 9/28/2022 Basis Expansions and Regularization (splines and RKHS) 第5週 10/5/2022 Basis Expansion and Regularization (RKHS and wavelet) Model Selection (bias-variance decomposition and prediction error) 第6週 10/12/2022 Model Selection (CV). GAM 第7週 10/19/2022 Model Selection (Bootstrap), Trees and Related Methods 第8週 10/26/2022 Boosting SVM 第9週 11/02/2022 Midterm Project SVM (kernel) Flexible discriminant analysis Penalized discriminant analysis Mixture discriminant analysis 第10週 11/09/2022 Clustering PCA Procrustes Transformation 第11週 11/16/2022 Principal Curves Kernel PCA Factor Analysis ICA Multidimensional Scaling Large p small n 第12週 11/23/2022 Functional Data Classification I 第13週 11/30/2022 Functional Data Classification II 第14週 12/7/2022 Paper Presentations 第15週 12/14/2022 Paper Presentations 第16週 12/21/2022 Final Project Presentations