| | The course both covers fundamental concepts in statistics and estimation (e.g., frequentist and Bayesian estimation, properties of estimators, and the bias-variance trade-off) and provides a rigorous treatment of machine learning topics from a probabilistic perspective (including regression, classification, clustering, graphical models, Markov models, variational inference, and MCMCs). Fluency in basic probability and familiarity with linear algebra are prerequisites. Please visit the website (to be updated) for more information. |
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ECE 6714