Linear regression. Maximum likelihood and maximum a posteriori estimation. Classification techniques, including na?ve Bayes, k-nearest neighbors, logistic regression, and support vector machines. Data visualization and feature extraction, including principal components analysis and linear projections. Clustering. Introduction to neural networks and deep learning. Discussion of other modern analysis methods.
FALL 2022 Schedule
Section |
Instructor |
Location |
Schedule |
Notes |
A1 |
Kulis |
EPC 205 |
MW 2:30 pm-4:15 pm |
|
FALL 2022 Schedule
Section |
Instructor |
Location |
Schedule |
Notes |
B1 |
Kulis |
PHO 202 |
F 10:10 am-11:00 am |
|
FALL 2022 Schedule
Section |
Instructor |
Location |
Schedule |
Notes |
B2 |
Kulis |
EPC 206 |
F 12:20 pm-1:10 pm |
|
SPRG 2023 Schedule
Section |
Instructor |
Location |
Schedule |
Notes |
A1 |
Cutkosky |
CAS 211 |
MW 2:30 pm-4:15 pm |
|
SPRG 2023 Schedule
Section |
Instructor |
Location |
Schedule |
Notes |
B1 |
Cutkosky |
EPC 208 |
F 10:10 am-11:00 am |
|
SPRG 2023 Schedule
Section |
Instructor |
Location |
Schedule |
Notes |
B2 |
Cutkosky |
EPC 208 |
F 12:20 pm-1:10 pm |
|
Note that this information may change at any time. Please visit the Student Link for the most up-to-date course information.