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STAT 462 Statistical Learning I
A working knowledge of the statistical software R is assumed. Classification; spline and smoothing spline; regularization, ridge regression, and Lasso; model selection; treed-based methods; resampling methods; importance sampling; Markov chain Monte Carlo; Metropolis-Hasting algorithm; Gibbs sampling; optimization. Given jointly with STAT 862.
Mathematics and Statistics
https://www.queensu.ca/academic-calendar/graduate-studies/programs-study/mathematics-statistics/
...sampling; optimization. (Offered jointly with STAT 462.) EXCLUSION: STAT 462 STAT 864 Discrete Time Series...
Mathematics and Statistics (MATH)
https://www.queensu.ca/academic-calendar/graduate-studies/courses-instruction/math/
...sampling; optimization. (Offered jointly with STAT 462.) EXCLUSION: STAT 462 STAT 864 Discrete Time Series...
Computing, Mathematics and Analytics – Specialization (Computing) – Bachelor of Computing (Honours)
...STAT 361 ; STAT 456 ; STAT 457 ; STAT 462 ; STAT 463 ; STAT 464 ; STAT 471 ; STAT...
Computing, Mathematics and Analytics – Specialization (Computing) – Bachelor of Computing (Honours)
...STAT 361 ; STAT 456 ; STAT 457 ; STAT 462 ; STAT 463 ; STAT 464 ; STAT 471 ; STAT...