CS 307
Spring 2026 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 227, MATH 257, MATH 415, MATH 416, ASRM 406.
| CRN | Type | Section | Time | Day | Location | Instructor | Section Details | |
|---|---|---|---|---|---|---|---|---|
|
71618
|
Discussion/
Recitation
Lecture
|
AL1
AL1
|
2:00PM
-3:15PM
2:00PM
-3:15PM
|
M
WF
|
1320 Digital Computer Laboratory
1320 Digital Computer Laboratory
|
Agyare, B
Demirtas, A Gonzalez, D Robles Granda, P Agyare, B
Demirtas, A Gonzalez, D Robles Granda, P |
|
|
|
73190
|
Discussion/
Recitation
Lecture
|
AL2
AL2
|
2:00PM
-3:15PM
2:00PM
-3:15PM
|
M
WF
|
1320 Digital Computer Laboratory
1320 Digital Computer Laboratory
|
Agyare, B
Demirtas, A Gonzalez, D Robles Granda, P Agyare, B
Demirtas, A Gonzalez, D Robles Granda, P |
|