This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.
This course explores electromagnetic phenomena in modern applications, including wireless and optical communications, circuits, computer interconnects and peripherals, microwave communications and radar, antennas, sensors, micro-electromechanical systems, and power generation and transmission. Fundamentals include quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided waves; resonance; acoustic analogs; and forces, power, and energy.
R. O'Donnell. (2021)cite arxiv:2105.10386Comment: First edition originally published April 2014, in hardcover book format by Cambridge University Press, and electronically on the author's website. This arXiv version corrects 100+ typos and errors, but is otherwise essentially the same.
A. Slivkins. (2019)cite arxiv:1904.07272Comment: The manuscript is complete, but comments are very welcome! To be published with Foundations and Trends in Machine Learning.
G. Peyré, and M. Cuturi. (2018)cite arxiv:1803.00567Comment: new version with corrected typo in Eq. 4.43 and 4.44 (minus sign in front of f, g now changed to +).