18-779 Stochastic Optimization
12 units, Carnegie Mellon University
Stochastic search algorithms are an important class of search techniques. They have proven valuable in a broad range of application areas, including in complex hardware design, solving NP-hard problems from scheduling and propositional logic, and in machine learning. They are also very useful in application domains where one needs to take multiple objectives (e.g., performance, weight, cost) into account when optimizing. This course is designed to give students a thorough grounding in the methods, theory, mathematics, and algorithms needed to do research and develop challenging applications that use stochastic search algorithms. The topics of the course draw from probability theory, statistics, electrical engineering, computer engineering, stochastic local search, evolutionary computation, machine learning, combinatorial optimization, and artificial intelligence. Applications will also be covered, as the course is project-oriented and is intended to give students hands-on experience with different stochastic search algorithms across a range of application areas.
Prerequisites: none listed
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