Machine Learning

Catalog description

This course offers a practical approach to harnessing the power of data using machine learning, with a strong emphasis on programming and implementation. The course covers a comprehensive range of supervised techniques, including regression, k-nearest neighbor, naïve Bayes, decision trees, tree ensembles, neural networks, and support vector machines. Students will also learn about unsupervised learning methods, such as association rules and clustering.