98年第1學期-4851 資料分析 課程資訊

課程分享

選課分析

本課程名額為 70人,已有17人選讀,尚餘名額53人。

評分方式

評分項目 配分比例 說明
Homeworks 30
Term paper 70 Either a thorough analysis of a large scale data or a software-development project, using techniques discussed in the course.

授課教師

呂恒輝

教育目標

該課程介紹多種創新統計方法(例如: Sliced Inverse Regressoin 與 Principal Hessian Directions etc.),用以達到維度縮減的目的.

課程概述

Dimensionality is an issue that can arise in every scientific field. Generally speaking, the difficulty lies on how to visualize a high dimensional function or data set. Aside from the differences that underlie the various scientific contexts, such kinds of questions do have a common root in Statistics. This is the driving force for the study of high dimensional data analysis. This course will discuss several statistical methodologies useful for exploring voluminous data. They include Principal Component Analysis, Clustering and Classification, Tree-structured analysis, Sliced inverse regression (SIR) and principal Hessian direction (PHD). SIR and PHD are two novel dimension reduction methods, useful for the extraction of geometric information underlying noisy data of several dimensions. The theory of SIR/PHD will be discussed in depth. It will be used as the backbone for the entire course. Examples from various application areas will be given. They include social/economic problems like unemployment rates, biostatistics problems like clinic trials with censoring, machine learning problems like handwritten digital recognition; quality control problems like performance measurement of digital to analogic converters; biomedical problems like functional Magnet Resonance Imaging, and bioinformatics problems like micro-array gene expression.

課程資訊

參考書目

No textbook. Lecture notes will be available.