Simple repository for course machine learning homework notebooks.
CS412_HW1_EnesKafa.ipynb- Builds a full classification workflow: dataset loading/splitting, exploratory analysis, preprocessing, k-NN training with hyperparameter tuning, final model evaluation, and confusion-matrix-based error analysis of misclassified classes.CS412_HW2_EnesKafa.ipynb- Focuses on regression fundamentals across two parts, including synthetic dataset generation, model fitting, model complexity and bias-variance behavior (e.g., higher-degree polynomial fits), and regularization using Ridge Regression.CS412_HW3_Enes_Kafa.ipynb- Implements an image classification pipeline with train/validation/test splitting, data preparation and visualization, transfer learning with VGG-16, fine-tuning/training, and final test-set evaluation (including confusion matrix review).hw4.ipynb- Analyzes a COVID-19 biomarker dataset with two probabilistic tasks: Maximum Likelihood Estimation (MLE) for parameter estimation and Naive Bayes classification for diagnosis-oriented prediction.