This repository contains assignments, projects, and practical examples from the CSE475 course on Machine Learning. It includes implementations of various supervised and unsupervised machine learning algorithms, along with ensemble techniques and explainable AI methods.
The repository is designed as a learning hub for understanding and applying machine learning algorithms to solve real-world problems.
- CSE475_Assignment_1_Ensemble_Learning_and_Explainable_AI:
- Focuses on ensemble learning methods like Bagging, Boosting, and Random Forests.
- Includes explanations of model decisions using tools like SHAP and LIME.
- CSE475_Lab_2_Unsupervised_Learning:
- Covers clustering algorithms like K-Means and Hierarchical Clustering.
- Contains CSV files and other data resources used for training and evaluating models.
- Implementations of machine learning techniques:
- Supervised Learning: Regression and classification models.
- Unsupervised Learning: Clustering, anomaly detection, and dimensionality reduction.
- Ensemble Learning: Advanced techniques for boosting and bagging.
- Explainable AI (XAI): Methods to interpret model outputs.
To run the code in this repository, make sure you have the following dependencies installed:
- Python 3.7+
- scikit-learn
- numpy
- pandas
- matplotlib
- seaborn
- Jupyter Notebook/Lab (optional)
- shap
- lime