Probability, Statistics, and Machine Learning

Catalog description

An introduction to the mathematical methods of data science through a combination of computational exploration, visualization, and theory. Students will learn scientific computing basics, topics in numerical linear algebra, mathematical probability (probability spaces, expectation, conditioning, common distributions, law of large numbers and the central limit theorem), statistics (point estimation, confidence intervals, hypothesis testing, maximum likelihood estimation, density estimation, bootstrapping, and cross-validation), and machine learning (regression, classification, and dimensionality reduction, including neural networks, principal component analysis, and unsupervised learning).