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.
This course may not be repeated for credit.
Prerequisite(s)
- Data Science 601, 602, 603, 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.
Sections
This course will be offered next in
Summer 2024.