EE 5110: Probability Foundations for Electrical Engineers
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In Fall 2021, I am offering an advanced graduate-level course on Probability Theory. This course will tentatively cover the following topics.
A. Probability Measures
- Measure Spaces, Sigma-Algebras
- Expectation, Convergence Theorems, Product Measures
- Conditional Expectation
B. Random Variables and their Distributions
- Moment Generating Functions
- Multivariate Normal Distribution and its properties
- Elementary Concentrations - Markov's inequality, Chebyshev's inequality
- Convergence of random variables, Law of Large Numbers
- Basics of Stochastic Processes
C. Applications
Evaluations
Problem-solving will be our primary vehicle for learning the material. We will have bi-weekly problem sets, a mid-term and a final project. The final grade will be a weighted average of these three components as detailed below:
- Problem Sets (45%)
- Mid-term (25%)
- Final Exam (30%)
Problem Sets
References
We will not follow any one particular source, in general. However, the following references will be useful