CS 441 AMO - Applied Machine Learning
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.
This course is only for students that are in the Computer Science MCS-DS Program. Additional ProctorU fees may apply. Description: The course is intended to support students who wish to apply machine learning methods, and will focus on tool-oriented and problem-oriented exposition. Application areas include computer vision, natural language, interpreting accelerometer data, and understanding audio data.
Academic Program Restrictions:
NDEG:Computer Science Onl-UIUC
Option 1Number of Required Visit(s): 0
Course Level: Graduate