課程大綱

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

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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

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.

參考書目

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