Applied Machine Learning ((4))

Catalog description

Students will learn the basics of machine learning including: supervised vs. unsupervised learning, regression, dimensionality reduction and reinforcement learning. Focus will be given to experimental setup including normalization, evaluation criteria and outlier detection. Experiments will be carried out with contemporary and classical methods on real world data sets in a wide range of applications. Each week lecture meets for 150 minutes and lab meets for 150 minutes. Cross-listed as CMPS 4550 or ECE 4550 .