Machine Learning

4 credits

This course provides a broad introduction to the practical side of machine learning and data analysis. The topics covered in this class include topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting and perceptrons, and topics in unsupervised learning, such as k-means, PCA, and Gaussian mixture models.