University of Illinois Urbana-Champaign 3 credits Computer Science
Introduction to probability theory and statistics with applications to computer science. Topics include: visualizing datasets, summarizing data, basic descriptive statistics, conditional probability, independence, Bayes theorem, random variables, joint and conditional distributions, expectation, variance and covariance, central limit theorem. Markov inequality, Chebyshev inequality, law of large numbers, Markov chains, simulation, the PageRank algorithm, populations and sampling, sample mean, standard error, maximum likelihood estimation, Bayes estimation, hypothesis testing, confidence intervals, linear regression, principal component analysis, classification, and decision trees. Credit is not given toward graduation for: Credit is not given for both CS 361 and ECE 313. Prerequisite: MATH 220 or MATH 221. Credit or concurrent registration in one of MATH 225, MATH 227, MATH 257, MATH 415, MATH 416, ASRM 406 or BIOE 210.
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