MSML603

Principles of Machine Learning

Restriction: Must be in one of the following programs: (Data Science Post-Baccalaureate Certificate, Master of Professional Studies in Data Science and Analytics, or Master of Professional Studies in Machine Learning). Cross-listed with: DATA603. Credit only granted for: DATA603, MSML603 or CMSC643. Formerly: CMSC643. A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning: Bayes decision theory, discriminant functions, maximum likelihood estimation, nearest neighbor rule, linear discriminant analysis, support vector machines, neural networks, deep learning networks. Unsupervised learning: clustering, dimensionality reduction, PCA, auto-encoders. The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.

Spring 2025

3 reviews
Average rating: 2.33

3 reviews
Average rating: 5.00

Fall 2024

16 reviews
Average rating: 4.19

3 reviews
Average rating: 2.33

3 reviews
Average rating: 5.00

14 reviews
Average rating: 2.93

Spring 2024

3 reviews
Average rating: 5.00

Past Semesters

6 reviews
Average rating: 4.83

0 reviews
Average rating: N/A

17 reviews
Average rating: 2.29

16 reviews
Average rating: 4.19

3 reviews
Average rating: 2.33

3 reviews
Average rating: 5.00

16 reviews
Average rating: 4.19

3 reviews
Average rating: 2.33

* "W"s are considered to be 0.0 quality points. "Other" grades are not factored into GPA calculation. Grade data not guaranteed to be correct.