Boston University - Spring 2025
Instructer: Deepti Ghadiyaram
This course provides a comprehensive introduction to machine learning fundamentals and advanced topics, covering classification (KNN, SVM, Naive Bayes), overfitting and regularization, decision trees and random forests, dimensionality reduction (PCA) and feature representation, clustering (K-means, Gaussian mixtures), regression (linear regression, boosting), graphical models (Markov chains, Hidden Markov Models), neural networks (MLP, CNNs, RNNs), attention mechanisms and Transformers, explainable AI (XAI), self-supervised learning, autoencoders, and generative models. Labs and quizzes reinforce practical skills using Numpy, Sklearn, and deep learning frameworks.