CS 307
Fall 2022 All Classes
All Classes
Credit: 4 hours.
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.
Prerequisite: STAT 207; one of MATH 225, MATH 257, MATH 415, MATH 416, ASRM 406.
Section Status updates every 10 minutes.
| CRN | Type | Section | Time | Day | Location | Instructor | Section Details | |
|---|---|---|---|---|---|---|---|---|
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77587
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Discussion/
Recitation |
D1
|
9:30AM
-10:45AM
|
F
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1302 Siebel Center for Comp Sci
|
Viswanathan, M
|
|
|
|
77586
|
Lecture
|
MLD
|
9:30AM
-10:45AM
|
MW
|
1302 Siebel Center for Comp Sci
|
Viswanathan, M
|
|