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.

University course exercises

Machine Educable Noughts and Crosses Engine - Revived

GridWorld Reinforcement Learning - Policy Iteration, Value Iteration.

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

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

Agent which computes the optimal policy for in a Dice Game

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.

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.