CS 307
CS 307 - Model & Learning in Data Sci
Spring 2027
| Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|---|
| Model & Learning in Data Sci | CS307 | ADA | 73189 | DIS | 0 | 0930 - 1045 | F | 4025 Campus Instructional Facility | |
| Model & Learning in Data Sci | CS307 | ADB | 78904 | DIS | 0 | 1400 - 1515 | F | 1302 Everitt Laboratory | |
| Model & Learning in Data Sci | CS307 | ADC | 78905 | DIS | 0 | 1530 - 1645 | F | 1302 Everitt Laboratory | |
| Model & Learning in Data Sci | CS307 | AL1 | 71618 | LEC | 4 | 0930 - 1045 | T R | 2079 Natural History Building | Daniel Gonzalez Cedre |
| Model & Learning in Data Sci | CS307 | AL2 | 73190 | LEC | 4 | 0930 - 1045 | T R | 2079 Natural History Building | Daniel Gonzalez Cedre |
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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.