Model Selection and Advanced Methods

This unit covers intermediate-to-advanced model selection, evaluation, and algorithmic techniques used in machine learning, including probabilistic approaches, support vector machines, deep learning (CNNs), clustering, dimensionality reduction, and ensemble methods. It builds on foundational concepts from earlier units and prepares students to optimize, interpret, and responsibly apply models in more advanced application and deployment contexts.

Learning Objectives

• Analyze and compare supervised and unsupervised algorithms (e.g., SVM, K-NN, K-Means) and probabilistic models by calculating and interpreting appropriate error and score metrics
• Apply validation and optimization techniques, including cross-validation, hyperparameter search, regularization, and gradient-based boosting, to improve model performance and reduce overfitting
• Demonstrate implementation and interpretation of deep learning and dimensionality-reduction methods (e.g., CNNs for image classification and SVD for feature extraction) for multimedia and text tasks
• Evaluate algorithmic trade-offs, common inference pitfalls, feature engineering choices, and ensemble strategies (e.g., averaging predictors, boosting) to recommend robust solutions aligned with ethical and practical constraints