EC ENGR C143A

Neural Signal Processing and Machine Learning

Description: Lecture, four hours; discussion, one hour; outside study, seven hours. Requisite: course 131A, Mathematics 33A. Topics include fundamental properties of electrical activity in neurons; technology for measuring neural activity; spiking statistics and Poisson processes; generative models and classification; regression and Kalman filtering; principal components analysis, factor analysis, and expectation maximization. Concurrently scheduled with course C243A. Letter grading.

Units: 4.0
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