This course covers reinforcement learning aka dynamic programming, which is a modeling principle capturing dynamic environments and stochastic nature of events. The main goal is to learn dynamic ...

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...

Most structural models for valuing corporate securities assume a geometric Brownian motion to describe the value of a firm’s assets. However, this does not reflect market stylized features: the ...

Dynamic Programming and Optimal Control is offered within DMAVT and attracts in excess of 300 students per year from a wide variety of disciplines. It is an integral part of the Robotics, System and ...

Figure 1: In vitro and in vivo expansion of OT-I CTLs. Figure 2: Abortive expansion in vivo is not overcome by overexpression of Bcl-2 or Bcl-x L. Figure 3: Sustained antigenic stimulation leads to ...

We present a novel method for deriving tight Monte Carlo confidence intervals for solutions of stochastic dynamic programming equations. Taking some approximate solution to the equation as an input, ...

Understand the principles of efficient algorithms for dealing with large scale data sets and be able to select appropriate algorithms for specific problems. Understand and be able to apply the main ...