CS 307
Fall 2025 Part of Term 1
Aug 25-Dec 10
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 | |
|---|---|---|---|---|---|---|---|---|
|
77586
|
Discussion/
Recitation
Lecture
|
M1
M1
|
9:30AM
-10:45AM
2:00PM
-3:15PM
|
F
TR
|
1320 Digital Computer Laboratory
3039 Campus Instructional Facility
|
Dalpiaz, D
Dalpiaz, D
|
|
|
|
77587
|
Discussion/
Recitation
Lecture
|
M2
M2
|
9:30AM
-10:45AM
2:00PM
-3:15PM
|
F
TR
|
1320 Digital Computer Laboratory
3039 Campus Instructional Facility
|
Dalpiaz, D
Dalpiaz, D
|
|
|
|
80859
|
Discussion/
Recitation
Lecture
|
M3
M3
|
11:00AM
-12:15PM
2:00PM
-3:15PM
|
F
TR
|
1320 Digital Computer Laboratory
3039 Campus Instructional Facility
|
Dalpiaz, D
Dalpiaz, D
|
|
|
|
80885
|
Discussion/
Recitation
Online Lecture
|
M4
M4
|
11:00AM
-12:15PM
ARRANGED
|
F
n.a.
|
1320 Digital Computer Laboratory
n.a.
|
Dalpiaz, D
Dalpiaz, D
|
|