PHYS476

Introduction to Applied Machine Learning

Reviews for this courseTestudo

Prerequisite: PHYS165, PHYS274, and PHYS276; or interested students with backgrounds in functional programming, linear algebra and statistics, should contact the instructors to request permission. Introduces machine learning techniques that are becoming pertinent in the technology industry. Focus on hands-on work using popular high-level libraries. Students are expected to have a background in functional programming, linear algebra, calculus, and mathematical modeling.

Past Semesters

Matt Severson

0 reviews
Average rating: N/A

Matthew Severson

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 2.75 between 37 students*

PHYS476 Grade Distribution+-05101520253035404550% of studentsABCDFWother
A-: 2.7%
A: 32.43%
A+: 10.81%
B+: 13.51%
C-: 2.7%
C: 2.7%
F: 5.41%
W: 16.22%
other: 13.51%
* "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.