CSC311 - Machine Learning Projects

This repository contains coursework for CSC311: Introduction to Machine Learning at Alexandria University, Faculty of Engineering.
Each assignment focuses on specific aspects of ML, progressing from fundamental algorithms to advanced deep learning techniques.

Instructor: Dr. Marwan Torki, Eng. Youssef El-Ebiary

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📂 Contents

Assignment 1 — Classification & Regression Fundamentals

• Manual and library-based K-NN classification.
• Manual and library-based Linear, Lasso, and Ridge regression.
• Data balancing, splitting, hyperparameter tuning, and evaluation.
• Analysis of overfitting and underfitting.

Assignment 2 — From Linear Models to Deep Networks

• Logistic and Softmax regression from scratch (PyTorch).
• Custom feedforward neural networks.
• Hyperparameter tuning and architecture experiments.
• Advanced techniques: CNN, dropout, batch normalization.

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🛠 Tools & Frameworks

• Python 3.x
• NumPy, Pandas, Matplotlib, Seaborn
• Scikit-Learn
• PyTorch
• Google Colab

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📌 Notes

• All projects include both manual implementations and framework-based implementations.
• Datasets and results are stored in their respective assignment folders.