MATH M37DS — Probability & Statistics for Data Science
Introduces statistical learning for data science. Emphasizes the following types of statistical models: Regression (Multiple Linear and Polynomial Regressions), Classification (Naive Bayes, Discriminant Analysis, Logistic Regression), Supervised Machine Learning (K-Nearest Neighbor, Tree models and their extensions), and Unsupervised Machine Learning (Principal Component Analysis, K-Means clustering). Covers applications of statistical programming for data science and the ethical use of data.