University of Iowa 3 credits ECE
Basic strategies to cope with noise in measurements are at the core of most machine learning tasks - hypothesis testing (where one must choose between various hypotheses), parameter estimation (where multiple parameters whose values define how a system will behave must be estimated from noisy measurements), and filtering (where noise must be removed from signals such as music); topics include probability and statistics, random variables and signals, hypothesis testing, parameter estimation, discrete- and continuous-time random processes, and optimal filtering; assignments, written exams, and projects. Prerequisites: STAT:2020 and ECE:2400.
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