Statistical models and algorithms for supervised and unsupervised learning; linear and logistic regression; classification and LDA; cross-validation and bootstrap; variable selection; ridge and LASSO penalization; smoothing splines and GAMs; SVM and kernels; CART and random forests; bagging; boosting; feed-forward and convolutional neural networks; k-means clustering and Gaussian mixtures; PCA.
Prereq: 6450, or permission of instructor. Not open to students with credit for 7620.