Physics
Machine Learning for Physics and Astronomy Data Analysis
3 credits
Fundamental principles and applications of data-centric research techniques in Physics and Astronomy. Topics include algorithms for data structuring, dimensionality reduction, linear regression and classification, artificial neural nets, convolutional neural nets, unsupervised learning.
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Average
80.8%
Students
95
Sections
3
Historical Averages
2023–2025
Lowest section avg
78.7%
Highest section avg
83.4%
Terms offered
3
Grade data from ubc-pair-grade-data.
| Section | Activity | Enrolled | Time | Instructor |
|---|---|---|---|---|
| 201 | Lecture | — | Mon 15:00-15:30, Wed 15:00-15:30 | Alison Lister |
| L2A | Laboratory | — | Mon 15:30-17:30, Wed 15:30-17:30 | Alison Lister |
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As published in the calendar: One of MATH 152, MATH 221, MATH 223 and one of MATH 200, MATH 217, MATH 226, MATH 253, MATH 254 and one of BIOL 300, STAT 200, STAT 203, STAT 241, STAT 251, MATH 318 and one of PHYS 210, CPSC 103, CPSC 110, APSC 160, EOSC 211 and one of PHYS 108, PHYS 118, PHYS 158, SCIE 001.
PHYS 310 corequisites
PHYS 310 antirequisites
PHYS 310 leads to