Capstone tutorial on Data-mining
Welcome to the capstone tutorial on data-mining 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 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.
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Introduction on component analysis
| Useful link | interesting video -
Principal component analysis
| An non-technical 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 5:20pm-7:30 Febuary 27, 2020
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White Board-based 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
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White Board-based mathematical justification
| Useful link | Extra tutorial -
K-means
| 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