AI / Machine Learning

AI Intro Course Labs and Programming Assignments

These projects were completed in Google Colab as part of AI coursework. They helped me build a practical foundation in classical machine learning, neural networks, data processing, and model evaluation.

Methods Covered

• Naive Bayes
• KNN
• K-Means Clustering
• MLP
• CNN
• Convolutional auto-encoders

Datasets and Tasks

• Sloan Digital Sky Survey data for celestial object classification and clustering analysis.
• Spotify song-feature data for K-Means clustering based on selected audio features.
• Anime rating prediction using Keras MLP with semantic, categorical, and continuous variables.
• Pokemon image classification and auto-encoder experiments, with 0.945 validation accuracy for classification.

Implementation Work

Based on provided skeleton code, I implemented core assignment components including pandas data loading, layer construction, model training, and result analysis.

Future Improvements

• Add GitHub links after cleaning course-code privacy issues.
• Add result plots, confusion matrices, and training curves.
• Rewrite selected labs as independent portfolio projects.