Introduction to Machine Learning

ENG EC 414

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.