CS 307
CS 307 - Model & Learning in Data Sci
Spring 2024
| Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|---|
| Model & Learning in Data Sci | CS307 | AL1 | 71618 | PKG | 4 | 1230 - 1345 | W F | 2036 Campus Instructional Facility | David M Dalpiaz |
| Model & Learning in Data Sci | CS307 | AL1 | 71618 | PKG | 4 | 1230 - 1345 | M | 2036 Campus Instructional Facility | David M Dalpiaz |
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Official Description
Introduction to the use of classical approaches in data modeling and machine learning in the context of solving data-centric problems. A broad coverage of fundamental models is presented, including linear models, unsupervised learning, supervised learning, and deep learning. A significant emphasis is placed on the application of the models in Python and the interpretability of the results. Course Information: Prerequisite: STAT 207; one of MATH 225, MATH 227, MATH 257, MATH 415, MATH 416, ASRM 406.