SCFGP: Sparsely Correlated Fourier Features Based Gaussian Process

Builds a sparse covariance matrix given a set of model parameter locations (x,z), and a correlation kernel

Finding out the most relevant features for pricing of a house

How does PCA work?

Homework and assignments for ENPM 673

Python ML/AI repository

FIFA'19 datasets Analysis

Code for the paper E. Raninen and E. Ollila, "Bias Adjusted Sign Covariance Matrix," in IEEE Signal Processing Letters, vol. 29, pp. 339-343, 2022, doi: 10.1109/LSP.2021.3134940.

This repository contains exercises from the Machine Learning course.

Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it …

Penalized precision matrix estimation via block-wise coordinate descent (graphical lasso)

This repository contains a Python implementation of Principal Component Analysis (PCA) for dimensionality reduction and variance analysis. PCA is a powerful statistical technique used to identify patterns in data by transforming it into a set of orthogonal (uncorrelated) components, ranked by the amount of variance they explain.

Penalized precision matrix estimation

This is an implementation of a custom-trained face detection classifier using the Viola Jones Algorithm cascaded with an Eigen Face recognition system based on Principal Component Analysis.

Contains some Test of Statistics

Evaluating machine learning methods for detecting sleep arousal, bachelor thesis by Jacob Stachowicz and Anton Ivarsson (2019)

Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.