Capstone tutorial on Data-mining

Welcome to the capstone tutorial on data-mining website for the 2020 winter session!

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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 open-ended real-world 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 5:20pm-7:30 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.

 

Component analysis 2

Tutorial location: | Biomedical library, Conference Room G18 5:20pm-7:30 Febuary 27, 2020

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Clustering

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The Curse of Dimensionality