Course Websites

CS 441 - Applied Machine Learning

Last offered Spring 2022

Official 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. Course Information: 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 and CS 361.

Related Faculty

Subject Area

Artificial Intelligence
TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Applied Machine LearningDSO73207ONL4 -    Marco Morales Aguirre
Pablo D Robles Granda
Applied Machine LearningMLG73206ONL3 -    Marco Morales Aguirre
Pablo D Robles Granda
Applied Machine LearningMLU73205ONL3 -    Marco Morales Aguirre
Pablo D Robles Granda