This repository contains my coursework assignments for the CSE 6363 Machine Learning course, part of my Master of Science in Computer Science program. This repository contains a collection of assignments demonstrating various concepts and techniques in Machine Learning.

Each folder in this repository corresponds to a specific assignment from the Machine Learning course. The contents include source code, outputs, and documentation for each task.

Assignments Overview

Assignment 1: Linear Models

• Focus: Linear Regression, Logistic Regression, Linear Discriminant Analysis
• Description: Implementation of linear models to understand the fundamentals of regression and classification in machine learning.

Assignment 2: Support Vector Machines

• Topics: SMO algorithm, SVM classification
• Description: Exploration of Support Vector Machines, including the Sequential Minimal Optimization (SMO) algorithm and its application in SVM classification.

Assignment 3: Neural Network Library

• Topics: Neural Networks, Backpropagation, Cross-validation
• Description: Development of a neural network library, allowing for the construction of networks with various layers and nodes, and understanding of backpropagation and cross-validation techniques.

Assignment 4: Decision Trees and Ensemble Methods

• Topics: Decision Trees, Random Forests, Boosting
• Description: Implementation of Decision Trees and study of ensemble methods including Random Forests and Boosting, focusing on various aspects like tree depth, sample splits, and criteria for splitting.

Assignment 5: Reinforcement Learning

• Topics: Q-Learning, Policy Iteration, OpenAI Gym
• Description: Implementation of Q-Learning and Policy Iteration on the Frozen Lake environment using OpenAI Gym, focusing on the basics of Reinforcement Learning.