DATA601

Designed to provide students with a deep, practical understanding of several of the most common and powerful analytical machine learning techniques in use by today’s data scientists. Regression as well as classification techniques are covered, including logistic regression, linear discriminant analysis, KNN, and shrinkage methods such as the Lasso. The importance of cross validation is discussed and its methods taught. Supervised as well as unsupervised learning is discussed. Taught using the R language.