CS 441
CS 441 - Applied Machine Learning
Fall 2025
Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
---|---|---|---|---|---|---|---|---|---|
Applied Machine Learning | CS441 | AMG | 74468 | LCD | 3 | 1230 - 1345 | T R | 0027/1025 Campus Instructional Facility | Derek W Hoiem |
Applied Machine Learning | CS441 | AMU | 74467 | LCD | 3 | 1230 - 1345 | T R | 0027/1025 Campus Instructional Facility | Derek W Hoiem |
Applied Machine Learning | CS441 | CSU | 79903 | ONL | 3 | - | Marco Morales Aguirre | ||
Applied Machine Learning | CS441 | DS3 | 81001 | ONL | 3 | - | Marco Morales Aguirre | ||
Applied Machine Learning | CS441 | DS4 | 74471 | ONL | 4 | - | Marco Morales Aguirre | ||
Applied Machine Learning | CS441 | MC3 | 80997 | ONL | 3 | - | Marco Morales Aguirre | ||
Applied Machine Learning | CS441 | MC4 | 79904 | ONL | 4 | - | Marco Morales Aguirre |
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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: One of CS 225 or CS 277, and one of CS 361, STAT 36
Subject Area
- Computational Materials