Catalog Description: This course connects classical statistical signal processing (Hilbert space filtering theory by Wiener and Kolmogorov, state space model, signal representation, detection and estimation, adaptive filtering) with modern statistical and machine learning theory and applications. It focuses on concrete algorithms and combines principled theoretical thinking with real applications.

Units: 3

Also Offered As: EECS 225A

Prerequisites: ELENG 120 and EECS 126

Formats:
Spring: 3 hours of lecture per week
Fall: 3 hours of lecture per week

Grading Basis: Student Option

Final Exam Status: Yes

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