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Machine Learning (in partnership with Stanford Engineering)

Machine Learning course includes the following subjects - univariateand multivariate linear regression, logistic regression, one-vs-all, regularization, neural networks and backpropagation, Support Vector Machines (SVMs), survey of such algorithms as naive bayes, decision trees and boosting, unsupervised learning mechanisms (such as K-means clustering, PCA and others), anomaly detections.

1. Neural Networks. The following movie shows my implementation of hand-written digits recognition by neural network - NN_digit_recognition.avi

2. Image Compression with K-means clustering:

Source code in Matlab.

3. PCA on Human Faces

Human faces' dimensionality reduction and reconstructing the faces from only the top 100 principal components. Source code in Matlab.



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