Data analysis using Principal Component Analysis (PCA), Eigenvalues, Covariance matrix, Maximum Likelihood Estimation (MLE), ISOMAP, & Image recognition
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Updated
Jul 6, 2024 - Python
Data analysis using Principal Component Analysis (PCA), Eigenvalues, Covariance matrix, Maximum Likelihood Estimation (MLE), ISOMAP, & Image recognition
A collection of the assignments in the course advanced machine learning
Optimal transport for comparing short brain connectivity between individuals | Optimal transport | Wasserstein distance | Barycenter | K-medoids | Isomap| Sulcus | Brain
Simple ISOMAP and PCA decomposition algorithms
Implementations of MAP, Naive Bayes, PCA, MDS, ISOMAP and some compression
Showcasing Manifold Learning with ISOMAP, and compare the model to other transformations, such as PCA and MDS.
Python package for plug and play dimensionality reduction techniques and data visualization in 2D or 3D.
Implementations of 3 linear and non-linear dimensionality reduction algorithms
The code for Multidimensional Scaling (MDS), Sammon Mapping, and Isomap.
Autoencoder model implementation in Keras, trained on MNIST dataset / latent space investigation.
a repository for my curriculum project
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can…
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