EE178
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Probabilistic Systems Analysis
Electrical EngineeringENGR - School of Engineering
Course Description
Introduction to probability and its role in modeling and analyzing real world phenomena and systems, including topics in statistics, machine learning, and statistical signal processing. Elements of probability, conditional probability, Bayes rule, independence. Discrete and continuous random variables. Signal detection. Functions of random variables. Expectation; mean, variance and covariance, linear MSE estimation. Conditional expectation; iterated expectation, MSE estimation, quantization and clustering. Parameter estimation. Classification. Sample averages. Inequalities and limit theorems. Confidence intervals.
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
4
Course Repeatable for Degree Credit?
No
Course Component
Lecture
Enrollment Optional?
No
Enforced Requisites
Prerequisite
Complete ALL of the following Courses:
- 2023541
- 1056441
- 1172571
Recommended: MATH 51 or equivalent level of calculus; CME 100 or equivalent; CS 106A or equivalent basic knowledge of computing.
This course has been approved for the following WAYS
Formal Reasoning (FR), Applied Quantitative Reasoning (AQR)
Does this course satisfy the University Language Requirement?
No
Schedule
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Courses
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Programs
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