Capstone tutorial on Datamining
Welcome to the capstone tutorial on datamining website for the 2020 winter session!
The Data Mining Capstone tutorial provides an opportunity for those researchers who have already taken multiple topic courses or research in the general area of data mining to further extend their mathematical knowledge and skills of data mining through both reading recent research papers and working on an openended realworld data mining project. This tutorial would be a great overview on some of the commonly used methods and algorithms for pattern recognition and machine learning, particularly for biomedical image applications.
Selected syllabes (jump to):
Component Analysis 1
Component Analysis 2
Clustering
The Curse of Dimensionality
Component analysis 1
Tutorial location:  Biomedical library, Conference Room G18 5:20pm7:30 January22, 2020
By: Bardia Yousefi
The term "Component analysis" refers to one of several topics in statistics and data analysis as techniques that converts a set of observations and atributes with possibly correlated variables, collinearity, into a set of values of linearly uncorrelated variables, or independent. In this tutorial, we are going to review the definition of principal component analysis (PCA) and how it will be used in different contexts such as data comparison, eigenvectors and eigenvalues, and formulating the problem.

Introduction on component analysis
 Useful link  interesting video 
Principal component analysis
 An nontechnical introduction on PCA  How to implement it in python 
Data compression
 Dimensionality Reduction Motivation  Image Compression via PCA. 
Problem formulation
 Principal component analysis with linear algebra 
Covariance matrix
 Mean Vector and Covariance Matrix  CompX: Mathematics of PCA  Covariance matrices 
Eigenvector & Eigenvalue
 Linear algebra  Eigenvectors and eigenvalues  Essence of linear algebra 
Singular value decomposition
 SVD with some exercises  SVD in MIT OpenCourseWare 
Access to slides of this chapter at :
Notes
Component analysis 2
Tutorial location:  Biomedical library, Conference Room G18 5:20pm7:30 Febuary 27, 2020

White Boardbased mathematical justification
 Useful link  Extra tutorial 
PCA algorithms
 PCA algorithm  go further 
Dual PCA
 The best tutorial  Some works done 
Kernel PCA
 Useful link  Extra reading 
CCIPCA
 Useful link  Extra tutorial 
Independent component analysis
 Lecture  Notes 
Applications of component analysis in different industries
 Art and archaeology  Interesting paper 
Access to slides of this chapter at :
Notes
UP
Clustering

White Boardbased mathematical justification
 Useful link  Extra tutorial 
Kmeans
 overview  Lecture 
Hierarchical clustering
 An overview  Some A short video 
MMD
 Useful link  Extra reading 
ISOMAP
 Useful link  Extra tutorial 
LLE
 Lecture  Notes 
Applications of clustering in different industries
 Implementation  Interesting paper 
Access to slides of this chapter at :
Notes
UP
The Curse of Dimensionality