https://github.com/amiraaz818/ml-assignments-2024

This repository archives assignments from the Machine Learning course at the University of Kerman, completed in Fall 2024.
https://github.com/amiraaz818/ml-assignments-2024

autoencoder decision-tree-classifier dimensionality-reduction exploratory-data-analysis ipynb linear-regression machine-learning neural-network python

Last synced: 3 months ago
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This repository archives assignments from the Machine Learning course at the University of Kerman, completed in Fall 2024.

• Host: GitHub
• URL: https://github.com/amiraaz818/ml-assignments-2024
• Owner: AmirAAZ818
• License: mit
• Created: 2024-09-25T18:46:39.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-08-15T08:30:53.000Z (3 months ago)
• Last Synced: 2025-08-15T10:18:24.458Z (3 months ago)
• Topics: autoencoder, decision-tree-classifier, dimensionality-reduction, exploratory-data-analysis, ipynb, linear-regression, machine-learning, neural-network, python
• Language: Jupyter Notebook
• Homepage:
• Size: 19.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Machine Learning Assignments - Fall 2024

## Overview

This repository serves as an archive of assignments completed for the Machine Learning course at the University of Kerman during Fall 2024. Each folder corresponds to a specific assignment, including both practical implementations and theoretical assignemnt solutions.

## Usage

The repository is organized to provide easy access to individual assignments, each contained within its own folder. **Each folder includes a dedicated README file** detailing the specific objectives, methods, and outcomes of the assignment. The code and reports are intended for educational purposes.

## Directory Structure

- **Analyzing Titanic Survival Rates - Practical**: Exploratory data analysis on the Titanic dataset.

- **Decision Tree - ID3 - Practical**: Implementation of the ID3 decision tree algorithm.

- **Linear Regression - Practical**: Practical application of linear regression with dataset analysis.

- **Noise Reduction with PCA and Autoencoders**: Noise reduction using PCA and Autoencoders on Fashion MNIST.

## Libraries Used

The assignments were implemented in Python, utilizing the following libraries:

- **NumPy**: For numerical computations and matrix operations.

- **Pandas**: For data manipulation and dataset handling.

- **Scikit-learn**: For machine learning algorithms (e.g., PCA, linear regression) and preprocessing.

- **PyTorch**: For neural network implementation (e.g., Autoencoders).

- **Matplotlib and Seaborn**: For data visualization and plotting.