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.
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L. Dinh, J. Sohl-Dickstein, und S. Bengio. (2016)cite arxiv:1605.08803Comment: 10 pages of main content, 3 pages of bibliography, 18 pages of appendix. Accepted at ICLR 2017.
F. Huszár, und D. Duvenaud. (2012)cite arxiv:1204.1664Comment: Accepted as an oral presentation at Uncertainty in Artificial Intelligence 2012. Updated to fix several typos.
L. Song, R. Shokri, und P. Mittal. (2019)cite arxiv:1905.10291Comment: ACM CCS 2019, code is available at https://github.com/inspire-group/privacy-vs-robustness.
M. Raginsky. (2011)cite arxiv:1110.0718Comment: 8 pages, uses ieeeconf.cls; to appear in Proc. 49th Annual Allerton Conf. on Communication, Control and Computing (2011).
H. Forssell. (2011)cite arxiv:1109.0699Comment: 32 pages. This is the first pre-print version, the final revised version can be found at http://onlinelibrary.wiley.com/doi/10.1002/malq.201100080/abstract (posting of which is not allowed by Wiley). Changes in v2: updated comments.
J. Brehmer, G. Louppe, J. Pavez, und K. Cranmer. (2018)cite arxiv:1805.12244Comment: Code available at https://github.com/johannbrehmer/simulator-mining-example . v2: Fixed typos. v3: Expanded discussion, added Lotka-Volterra example. v4: Improved clarity.
S. Kaur, J. Cohen, und Z. Lipton. (2019)cite arxiv:1910.08640Comment: To appear in the "Science Meets Engineering of Deep Learning" Workshop at NeurIPS 2019.