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Sümeyye's Projects

expectation-maximization-algorithm-from-scratch icon expectation-maximization-algorithm-from-scratch

Implement Expectation-Maximization(EM) Algorithm on Gaussian Mixture Models to cluster the given data within the files. Plot the data points on a scatter plot using a unique color for each cluster. Repeat the procedure for K ∈ {2, 3}.

facets icon facets

Visualizations for machine learning datasets

k-nn-smoother icon k-nn-smoother

Implement k-NN Smoother using Gaussian kernel and a fixed number of neighbors (k). Use Mahalanobis distance.

lda-from-scratch icon lda-from-scratch

An implementation of Linear Discriminant Analysis(LDA) from scratch for OptDigits dataset

mlp-autoencoder icon mlp-autoencoder

Implement multilayer perceptrons (MLP) that contains a hidden layer and tanh activation function(only hidden layer) with 64 input, 2 hidden, and 64 output units. Train the network using stochastic gradient descent with mini-batches and use mean-square-error (MSE) as the loss function. Train your MLP for multiple epochs with optdigits.tra and visualize the output of the hidden layer on a scatter plot.

nonparametric-classification-gaussian-kernel icon nonparametric-classification-gaussian-kernel

Implement the multivariate nonparametric classification algorithm with Gaussian kernel from scratch. Use Mahalanobis distance to normalize the variances. S denotes the covariance matrix of the training set.

pca-from-scratch icon pca-from-scratch

An implementation of Principal Component Analysis(PCA) from scratch for OptDigits dataset, and visualization

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