CMSC422

Introduction to Machine Learning

Prerequisite: Minimum grade of C- in CMSC320, CMSC330, and CMSC351; and 1 course with a minimum grade of C- from (MATH240, MATH461); and permission of CMNS-Computer Science department. Machine Learning studies representations and algorithms that allow machines to improve their performance on a task from experience. This is a broad overview of existing methods for machine learning and an introduction to adaptive systems in general. Emphasis is given to practical aspects of machine learning and data mining. Credit only granted for CMSC422 or CMSC498M.

Spring 2026

6 reviews
Average rating: 3.17

17 reviews
Average rating: 3.47

Fall 2025

17 reviews
Average rating: 2.00

17 reviews
Average rating: 3.47

4 reviews
Average rating: 2.75

Summer 2025

17 reviews
Average rating: 3.47

Spring 2025

6 reviews
Average rating: 3.33

17 reviews
Average rating: 3.47

Past Semesters

17 reviews
Average rating: 3.47

4 reviews
Average rating: 2.75

17 reviews
Average rating: 2.00

6 reviews
Average rating: 3.17

17 reviews
Average rating: 3.47

17 reviews
Average rating: 3.47

17 reviews
Average rating: 3.47

5 reviews
Average rating: 3.60

6 reviews
Average rating: 3.17

17 reviews
Average rating: 3.47

6 reviews
Average rating: 3.17

5 reviews
Average rating: 3.60

5 reviews
Average rating: 2.60

6 reviews
Average rating: 3.33

1 review
Average rating: 5.00

1 review
Average rating: 2.00

0 reviews
Average rating: N/A

0 reviews
Average rating: N/A

During the Spring 2020 and Spring 2021 semesters, students could choose to take some of their courses pass-fail mid-semester which skews grade data aggregated across multiple semesters.

Average GPA of 3.17 between 3,342 students*

CMSC422 Grade Distribution+-05101520253035404550% of studentsABCDFWother
A-: 12.72%
A: 19.75%
A+: 13.49%
B-: 8.14%
B: 15.95%
B+: 11.49%
C-: 2.99%
C: 5.8%
C+: 4.79%
D-: 0.12%
D: 0.6%
D+: 0.27%
F: 0.87%
W: 2.96%
other: 0.06%
* "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.