A general classifier module to allow Bayesian and other types of classifications. A fork of cardmagic/classifier.

Statistical pattern recognition course projects from shiraz university.

Hyperspectral image Processing and Classification. This repository provides my solution for Project assignment for the course of Machine Learning and Computational Statistics for the MSc in Data Science at Athens University of Economics and Business.

This repositories contains implementation of various Machine Learning Algorithms such as Bayesian Classifier, Principal Component Analysis, Fisher Linear Discriminator, Face Recognition and Reconstruction, Gaussian Mixture Model based Segmentation, Otsu's Segmentation, Neural Network etc. Relevant data sets and results are also included.

A proof-of-concept implementation of a blackbox $f$-DP estimator/auditor framework

Binary classification of images using Bayes classifier

Implementation of various Data Warehouse and Mining algorithms and techniques like Apriori, Bayesian classification, KMeans and ETL processes

Using NLTK's Bayesian Classification to classify any roman string as English, Spanish or Japanese

These are computer assignments of the book "An Introduction to Machine Learning" by Miroslav Kubat

Accuracy comparison between generative and discriminative models

Statistical Pattern Recognition: PCA and Bayesian Classifiers

Project involved the building and training of machine learning models using the custom built kNN and bayesian classifier as well as the classifiers from the python sklearn library.

Implementation of a sketch‐recognition pipeline inspired by Google’s Quick, Draw!. Includes data preprocessing and feature‐engineering scripts, three Bayesian classifiers alongside Logistic Regression, SVM, K-NN and XGBoost baselines, and an RNN model.

Tripadvisor review sentiment analysis for final data science project

Public archive for previous private project on AI and Bayesian Classifier, selected files displayed.

PL Klasyfikacja Bayesowska (bayesian-language-classifier)

Contains code to run and visualize techniques like Clustering, PCA, Generative Modeling on publicly available data.

Binary classification project in Python comparing a MAP classifier (with known distributions) vs. logistic regression. Includes analytical posterior derivation, Monte Carlo simulation, and gradient-based training. Focus on error probability, variance impact, and model performance.