Theory and Methods of Statistical Learning and Inference


Estimators and properties (efficiency, consistency, sufficiency); loss functions. Fisher information, asymptotic properties and distributions of estimators. Exponential families. Point and interval estimation, delta method. Neyman-Pearson lemma; likelihood ratio tests; multiple testing; design and the analysis of variance (ANOVA). High-dimensional data; statistical regularization and sparsity; penalty and prior formulations; model selection. Resampling methods; principal component analysis, mixture models.

Prerequisites: (STA240, STA230, or STA231). Suggested: STA 210, STA360, (MATH 221, MATH 218, or MATH 216)

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