Machine learning is the process of making predictions and decisions from data without being explicitly programmed. Topics include a variety of supervised learning methods. Ensemble approaches are used to combine independent models efficiently. Unsupervised and semi-supervised methods demonstrate the power of learning from data without an explicit training goal.
Prerequisite: (COMP 231 or COMP 271) and (MATH 131 or MATH 161) and (STAT 103 or STAT 203 or ISSCM 241 or PSYC 304 or instructor permission).
Outcomes: Students in this course will learn how to apply sophisticated algorithms to large data sets to make inferences for prediction or decision making.
Prerequisite: (COMP 231 or COMP 271) and (MATH 131 or MATH 161) and (STAT 103 or STAT 203 or ISSCM 241 or PSYC 304 or instructor permission).
Outcomes: Students in this course will learn how to apply sophisticated algorithms to large data sets to make inferences for prediction or decision making.