課程資訊
課程名稱
統計學習
Statistical Learning 
開課學期
111-1 
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
限碩士班以上 且 限本系所學生(含輔系、雙修生)
總人數上限:30人 
 
課程簡介影片
 
核心能力關聯
核心能力與課程規劃關聯圖
課程大綱
為確保您我的權利,請尊重智慧財產權及不得非法影印
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 
參考書目
 
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. 
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
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