ENGRCEE 290B
Machine, Model, and Statistical Learning II
Advanced mathematical concepts in machine, model, and statistical learning. Linear algebra, information, probability, and set theory. Topics include sensitivity analysis, forecast verification, model selection, model averaging, data assimilation (Kalman filter), and sequential Monte Carlo (particle filter).
Topics
- sensitivity analysis
- forecast verification
- model selection
- model averaging
- data assimilation (Kalman filter)
- and sequential Monte Carlo (particle filter)
Topics explicitly listed in official catalogue description
Prerequisites & course information
Advanced mathematical concepts in machine, model, and statistical learning. Linear algebra, information, probability, and set theory. Topics include sensitivity analysis, forecast verification, model selection, model averaging, data assimilation (Kalman filter), and sequential Monte Carlo (particle filter).
Sources
- Course catalogue ↗2026-2027