Announcement

In 2023-2024 academic year, EE178 will be offered only in the Spring quarter.

Description

Real-world phenomena and systems are probabilistic in nature: the outcome of an experiment is uncertain; when an input is applied to a system, the output is not predictable. Examples are all around us from gambling and the financial markets, sports, medical diagnosis and spread of disease, electronic devices, communication and storage systems, Internet traffic and social networks, renewable energy, polling and elections, climate and evolution, to statistical and quantum physical systems. The modeling and analysis of probabilistic systems involve the fields of probability theory, statistics, machine learning and statistical signal processing. 

This course covers the basic concepts and techniques of probability theory with applications to statistics, machine learning and statistical signal processing. Examples and homework problems are drawn from many fields. To see probability in action and to demonstrate the process of probabilistic modeling and analysis, the homework sets include computational problems in Python, some with real data.

Topics include (see syllabus)

• Axioms of probability, conditional probability, Bayes rule, independence 

• Random variables
  Applications: Signal detection, parameter estimation, classification 

• Expectation, conditional expectation
  Applications: Linear and nonlinear MSE estimation, quantization

• Inequalities and limit theorems, confidence intervals

Reading

• Lecture slides (will be posted on the Canvas class webpage)

• Lecture videos from Spring 2021

Class website