Description: Lecture, four hours; outside study, eight hours. Preparation: prior training in probability theory, random processes, linear algebra, and adaptation. Enforced requisite: course 210A. Adaptation, learning, estimation, and detection over networks. Steepest-descent algorithms, stochastic-gradient algorithms, convergence, stability, tracking, and performance analyses. Distributed optimization. Online and distributed adaptation and learning. Synchronous and asynchronous network behavior. Incremental, consensus, diffusion, and gossip strategies. Letter grading.

Units: 4.0