Room 3178, Department of Mathematics, NCKU
Speaker(s):
Chih-Wei Chen (NCTS)
Mao-Pei Tsui (National Taiwan University)
Organizer(s):
Chih-Wei Chen (NCTS)
River Chiang (National Cheng Kung University)
Matthew M. Lin (National Cheng Kung University)
Yu-Chen Shu (National Cheng Kung University)
Mao-Pei Tsui (National Taiwan University)
一、 課程背景與目的：
Manifold learning encompasses much of the disciplines of geometry, computation, and statistics, and has become an important research topic in data mining and statistical learning. The simplest description of manifold learning is that it is a class of
algorithms for recovering a low-dimensional manifold embedded in a high-dimensional ambient space. This course aims to help those who want to understand the geometric aspects of various learning algorithms.
PCA (Principal Component Analysis), diffusion map and MDS (Multidimensional Scaling) are three important topics in manifold learning theory. We will introduce the theories and implementations of these topics. The prerequisite of this mini-course is linear algebra.
二、 課程之大綱：
There are lectures on PCA, diffusion map, and MDS. All lectures are accompanied with basic programming (using Matlab). The second day is for discussions on implementation.
三、 課程詳細時間：
10:30-12:00 PCA
12:00-13:30 Lunch break
13:30-14:20 Diffusion Maps (I)
14:20-14:40 Break
14:40-15:30 Diffusion Maps (II)
15:30-15:50 Break
15:50-17:20 MDS
2020/07/24
09:00-10:30 Coding practice
10:50-12:00 Idea exchange
