Statistical and Machine Learning

3 credits · Graduate Programs

Latent variable models for clustering and dimension reduction. Parametric and nonparametric methods for regression and classification including naïve Bayes, decision trees, random forests, and boosting. Model assessment and selection. Deep learning. Prerequisite(s): Data Science 601, 602, 603 and 604, and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science or Financial and Energy Markets Data Modelling.