Machine Learning
Prerequisites: (COMP 231 or COMP 271) and (MATH 131 or MATH 161) and (STAT 103 or STAT 203 or ISSCM 241 or STAT 335 or PSYC 304 or instructor permission).
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.
Outcomes: Students in this course will learn how to apply sophisticated algorithms to large data sets to make inferences for prediction or decision making.
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.
Outcomes: Students in this course will learn how to apply sophisticated algorithms to large data sets to make inferences for prediction or decision making.