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

This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.

An implementation of Contract-Net Protocol in an attacker/defender scenario

Symbolic compilation of RDDL domains, Dynamic Bayes net (DBN) visualization, symbolic dynamic programming (SDP).

Implementation of certain crucial algorithms in the field of reinforcement learning.

GridWorld Reinforcement Learning - Policy Iteration, Value Iteration.

University course exercises

Machine Educable Noughts and Crosses Engine - Revived

Artificial Intelligence course, Computer Science M.Sc., Ben Gurion University of the Negev, 2021

Computing optimal MDP policy using Value Iteration Algorithm and Linear Programming

A modernized, interactive demo of value iteration in a 10×10 grid world, adapted from David Poole’s original demo. Visualizes how the value function and optimal policy evolve with each iteration.

Agent which computes the optimal policy for in a Dice Game

Implementation of a basic Q Learning algorithm in the OpenAI's gym environment

Lab 8: Reinforcement Learning

An implementation of the Value Iteration algorithm for solving the Grid World problem. This project provides a function to compute the optimal value function for a grid-based environment where a robot navigates to maximize rewards while avoiding penalties.

This repository contains a practical application of Infinite Horizon Dynamic Programming (IHDP) techniques, demonstrated through the Frozen Lake environment and grid world examples. The repository includes a Jupyter Notebook that explores these techniques with visual aids.

TLDR: Generic Algorithms, Decision Trees, Value Iteration, POMDPs, Bias-Variance. Data preprocessing using statistical techniques and visualization is crucial to understand and analyze the data before utilizing them to train a machine learning model. Several fundamental techniques for preprocessing are presented here.