Probability, Statistics and Machine Learning

3 credits · Undergraduate Programs

Presentation and description of data, introduction to probability theory, Bayes' theorem, discrete and continuous probability distributions, estimation, sampling distributions, tests of hypotheses on means, variances and proportions; Introduction to fundamental machine learning including linear regression, classification and correlation. Applications are chosen from engineering practice from all disciplines. Prerequisite(s): Mathematics 277 or 331; and one of Engineering 233, Digital Engineering 233 or 440. Antirequisite(s): Credit for Digital Engineering 319 and Biomedical Engineering 319 will not be allowed. Also known as: (formerly Engineering 319)