STA 6703
Statistical Machine Learning
Methodology and application of tools of statistical (machine) learning targeted at graduate students in engineering, applied statistics/biostatistics and quantitative life sciences. Statistical approaches to machine learning are emphasized. Application and the intuition behind statistical methods rather than formal derivations and full mathematical proofs of the procedures are prioritized.
Prerequisites & course information
Grading Scheme: Letter Grade Prerequisite: Calculus-based probability and statistics (e.g., EGN6446 or PHC6092 or STA5328). Additionally, knowledge of scientific/statistical computing (e.g., in R or Python) and undergraduate mathematics (multivariate calculus and linear algebra).
Sources
- Course catalogue ↗2025-2026