ELE382: Probabilistic Systems and Information Processing

Course Description

A wide spectrum of engineering applications require efficient procedures to describe, process, analyze, and infer the signals / data of interest, which are often accomplished by imposing proper statistical models on the objects under consideration. This course introduces the fundamental statistical principles and methods that play a central role in modern information processing. Specific topics include random processes, linear regression and estimation, hypothesis testing and detection, and graphical models.

Lectures

• Mon, Wed 1:30 PM - 2:50 PM.

• Room: Friend Center 005

Teaching Staffs

• Instructor: Yuxin Chen, B316 Equad, yuxin dot chen at princeton dot edu

• Teaching assistant: Qingcan Wang, 218 Fine hall, qingcanw at princeton dot edu

Announcements

• 9/20: Homework 1 is out (see Blackboard). It is due on Wednesday, Sep 26.