Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

Multi-Layer Random Forest, with Circularity Features, for Image Segmentation

Multi-Layer Random Forest [for Image] Segmentation

Submitted paperwork

EE6227 Genetic Algorithms & Machine Learning

This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.

Thesis project concerning the development of predictive regression models for the estimation of cetacean abundance in the Gulf of Taranto using climatic and morphological variables.

Yann's Matlab Codes for ML

A single-layer Random Forest model for voxel classification (volume segmentation).

These functions are included the "Random Forest" and the hybrid Random Forest and Multi-Objective Particle Swarm Optimization ("RF_MOPSO") to predict the targets as learning approach and find the optimal parameters of a multi-feature process, respectively. The example of this version is a drilling process prediction and optimization. Instruction…

Heart-Disease dataset analysis using Matlab and the Orange framework.

Repository with data and code for the prediction of RAP DoA using Compaction Temperature, Air voids and ITS with machine learning techniques

Pitch Detection comparsion with Machine learning Algorithms

Used survey data with personality traits to predict cannabis consumption.

This repository demonstrates the the comparison of Naïve Bayes and Random Forest models applied to the Red Wine Dataset from UCI.

CMCC Machine Learning and Deep Learning experiments to predict salinity concentration at the estuary mouth.

Preliminary investigation of machine learning techniques to perform parameters estimation for cube type crystal structure.

Code written for MATH 444 projects Spring 2021

CMCC-Hybrid-EBM collects all the experiments releated to the estimation of salt-wedge intrusion length and salinity concentration using hybrid and machine learning based approaches.