Online Course Catalog
CS 441 - Applied Machine Learning
Spring 2024
| Title | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|
| Applied Machine Learning | CSP | 75817 | ONL | 3 | - | Marco Morales Aguirre | ||
| Applied Machine Learning | DSO | 73207 | ONL | 4 | - | Marco Morales Aguirre | ||
| Applied Machine Learning | MC3 | 76074 | ONL | 3 | - | Marco Morales Aguirre | ||
| Applied Machine Learning | MCS | 75362 | ONL | 4 | - | Marco Morales Aguirre |
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.
Prerequisites
CS 225 and CS 361.