Machine Learning

Catalog description

Theory and practical use of machine learning techniques, such as decision trees, logistic regression, discriminant analysis, neural networks, naive Bayes, k-nearest neighbor, support vector machines, collaborative filtering, clustering, and ensembles. Emphasizes hands-on experience with real-world datasets, combined with several programming projects.