IN 404: Machine Learning

Provides a comprehensive study of machine learning algorithms and practical implementations. Covers supervised learning (linear regression, logistic regression, decision trees, random forests, SVM), unsupervised learning (k-means, PCA), model validation metrics, cross-validation, and hyperparameter tuning.

• Supervised regression models: ordinary least squares (OLS), Ridge, Lasso, and polynomial regression in IN 404
• Supervised classification algorithms: logistic regression, support vector machines (SVM), and Random Forests for Machine Learning
• Model evaluation and validation: ROC-AUC curves, confusion matrices, precision, recall, and k-fold cross-validation in IN 404
• Unsupervised learning and dimensionality reduction: Principal Component Analysis (PCA) and t-SNE at Purdue Global