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 ...

This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) ...

Inspired by their win at Brandeis University’s DeisHack, Marco Qin ’24 and Mahmoud Salah ’24 have taken their coding talents to some of the most competitive college Hackathon events in the country.

In addition to other methods we’ve discussed, a third type of variable spending model uses dynamic programming methods. These methods rely on complex computing power and mathematical equations to ...

Dynamic programming algorithms are developed for optimal capital allocation subject to budget constraints. We extend the work of Weingartner [17] and Weingartner and Ness [19] by including multilevel ...