Optimal learning

Description

The challenges of learning -- Adaptive learning -- The economics of information -- Ranking and selection -- The knowledge gradient -- Bandit problems -- Elements of a learning problem -- Linear belief models -- Subset selection problems -- Optimizing a scalar function -- Optimal bidding -- Stopping problems -- Active learning in statistics -- Simulation optimization -- Learning in mathematical programming -- Optimizing over continuous measurements -- Learning with a physical state.;"This text presents optimal learning techniques with applications in energy, homeland security, health, sports, transportation science, biomedical research, biosurveillance, stochastic optimization, high technology, and complex resource allocation problems. The coverage utilizes a relatively new class of algorithmic strategies known as approximate dynamic programming, which merges dynamic programming (Markov decision processes), math programming (linear, nonlinear, and integer), simulation, and statistics. It features mathematical techniques that are applicable to a variety of situations, from identifying promising drug candidates to figuring out the best evacuation plan in the event of a natural disaster"--