CSCE 5215 - Machine Learning

3 hours

The theory and process to create systems that learn directly from data to make predictions and decisions. Topics include a wide variety of supervised learning methods, both regression and classification, with an emphasis on those that perform well on large feature sets. Ensemble methods are used to combine independent approaches efficiently. Unsupervised and semi-supervised methods demonstrate the power of learning from data without an explicit training target or goal. Reinforcement learning enables effective reward-seeking behaviors in complex environments. The goal is to create models that can make automated decisions from new data, or make inferences on unlabeled data to aid in understanding and future prediction models.

Prerequisite(s): None.

Course specific fees (in addition to tuition and mandatory):
Academic (AF) per hour: $73.70