CS 441: Applied Machine Learning

Course Description

Techniques of machine learning to various signal problems: regression, including linear regression, multiple regression, regression forest and nearest neighbors regression; classification with various methods, including logistic regression, support vector machines, nearest neighbors, simple boosting and decision forests; clustering with various methods, including basic agglomerative clustering and k-means; resampling methods, including cross-validation and the bootstrap; model selection methods, including AIC, stepwise selection and the lasso; hidden Markov models; model estimation in the presence of missing variables; and neural networks, including deep networks. The course will focus on tool-oriented and problem-oriented exposition. Application areas include computer vision, natural language, interpreting accelerometer data, and understanding audio data. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 225 or CS 277, and one of CS 361, STAT 361, ECE 313, BIOE 310, MATH 362, MATH 461, MATH 463 or STAT 400.