EECS 225A. Statistical Signal Processing

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: 3.0

Prerequisites: ELENG 120 and EECS 126

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

Grading basis: letter

Final exam status: Written final exam conducted during the scheduled final exam period


Class Schedule (Spring 2020):
TuTh 2:00PM - 3:29PM, Cory 293 – Jiantao Jiao

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