課程資訊
課程名稱
 統計學習
Statistical Learning 
開課學期
 112-1 
授課對象
 理學院  統計與數據科學研究所  
授課教師
 江其衽 
課號
 STAT5009 
課程識別碼
 250 U0090 
班次
  
學分
 3.0 
全/半年
 半年 
必/選修
 選修 
上課時間
 星期三6,7,8(13:20~16:20) 
上課地點
 新502 
備註
 總人數上限：30人 
  
課程簡介影片
 
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課程概述

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. 

課程目標
 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. 
課程要求
 Calculus, Linear Algebra, and Statistics 
預期每週課後學習時數
  
Office Hours
 另約時間 備註： Monday 10--11am or By appointment 
指定閱讀
 Hastie, T., Tibshirani, R. and Friedman, J. (2016). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition. Springer. https://hastie.su.domains/ElemStatLearn/ 
參考書目
 James, Witten, Hastie and Tibshirani (2021). An Introduction to Statistical Learning with Applications in R. 2nd Edition. Springer. https://www.statlearning.com 
評量方式
(僅供參考)
    
針對學生困難提供學生調整方式
  
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
 09/06/2023  Introduction
Overview of Supervised Learning 
第2週
 09/13/2023  Linear Methods for Classification: (1) Linear Regression, (2) LDA, (3) QDA, and (4) Reduced-rank LDA 
第3週
 09/20/2023  Linear Methods for Classification: (5) Logistic Regression and (6) Separating Hyperplanes
Basis Expansions and Regularization: (1) Piecewise Polynomials and Splines 
第4週
 09/27/2023  Basis Expansions and Regularization: (1) Piecewise Polynomials and Splines, (2) B-spline, (3) Smoothing Splines, (4) Nonparametric Logistic Regression 
第5週
 10/04/2023  Basis Expansions and Regularization: (5) Multidimensional Splines, (6) RKHS, (7) wavelet
Kernel Smoothing Methods: (1) Empirical CDF, (2) Kernel Density Estimator 
第6週
 10/11/2023  Kernel Smoothing Methods: (2) Kernel Density Estimator, (3) Local Polynomial Regression 
第7週
 10/18/2023  Varying Coefficient Models
Model Selection 
第8週
 10/25/2023  EM Algorithm
Generalized Additive Models
Trees 
第9週
 11/01/2023  Trees
Multivariate Adaptive Regression Splines
Hierarchical Mixtures of Experts
Boosting
SVMs 
第10週
 11/08/2023  kernel SVMs
Flexible Discriminant Analysis
Penalized Discriminant Analysis
Mixture Discriminant Analysis
Functional Data 
第11週
 11/15/2023  Functional PCA 
第12週
 11/22/2023  Depth and Distance
Functional LDA 
第13週
 11/29/2023  Functional LDA
Functional GLM
Inverse Regression
Clustering 
第14週
 12/06/2023  Oral Presentations 
第15週
 12/13/2023  Oral Presentations 
第16週
 12/20/2023  Final Project