Machine Learning

3 credits

Understand, develop, and apply mechanisms for supervised, unsupervised, and reinforcement learning. Selection of a proper machine learning algorithm for a problem, data preprocessing, evaluation techniques, interpretation of the resulting models, explaining model shortcomings. Biased data sets and other sources of error. Ethical and societal implications of machine learning to practical problems. ***Prerequisite: CS 320.***