This is a theoretical course on machine learning. It will cover Lebesgue integration, weak and strong laws of large numbers,
consistency, asymptotic normality, finite-time
bounds, probabilistic almost correct learning and theoretical properties of learning methods based on simulation.
The course is targeted at mathematically inclined students.
The requirements for registering
are either CPSC540 or an A+ in CPSC340.
Mon 2:00-3:30pm, Wed 12:30-2:00pm
Nando de Freitas (nando at cs)
Anytime for registered students (ICICS 183).
All of Statistics.
A Probabilistic Theory of Pattern Recognition.
Prediction, Learning, and Games.
Simulation-Based Econometric Methods.