The Data Science Labs on Multivariable Calculus (MA290/MA26190/ECE29595):
Fall 2023 offering (pick one):
Tu 3:00-5:30pm CRN 27738
Tu 3:00-5:30pm CRN 25032 (for ECE students)
Th 5:30-8pm CRN 19789
Th 5:30-8pm CRN 25033 (for ECE students)
A one credit course to accompany Calculus 3. Discover applications of multivariable calculus to data science. You will also practice programming in Python and use Arduino sensors and microprocessors to acquire data. The lab counts as a complementary elective for ECE (CMPE and BSEE).
Prerequisite: MA16290 or prior experience with Python
Co-requisite Calculus 3 (MA261 or MA 271)
The class requires no work outside of the lab. There is no homework, no quiz, no test, no exam. All work is performed during the 150 minutes spent in the lab each week. Students are free to leave as soon as they hand in their report. If you do well in the class and display good collaboration and communication skills, you may be invited to become a (paid) TA for the course in future semesters.
The book for the class is available on github at:
https://thedatasciencelabs.github.io/DataLab_Multivariate_Calculus
If you are taking Calculus 3 (MA261) at the same time as this lab, you can earn honors credit for Calculus 3 (4 credits) by taking this lab. See syllabus for details.
Lab Topics by Week
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• Introduction
Week 1: Syllabus, getting started
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• Color and Greyscale
Week 2: Color as a vector
Week 3: Human perception of color
Week 4: Color quantization
Week 5: Detect the color of M&Ms
Week 6: Edge detection in images
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• Video
Week 7: Acquiring and manipulating videos
Week 8: Optical flow
Week 9: Motion detection
Week 10: Real-time motion detection
Week 11: M&M detection
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• Green’s theorem
Week 12-13 Build a planimeter
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• Design a project of your own
Week 14-15: Final project
Acknowledgements
This course is offered through the support of the Elmore Family School of Electrical and Computer Engineering. The hardware and development was supported by the Engineering Honors Program and the Department of Mathematics. We thank Prof. Alina Alexeenko, Prof. Eric Nauman , Prof. Kristina Bross, Prof. Milind Kulkarni, Prof. Uli Walther, Dr. Natasha Duncan, and Ben Manning for their invaluable input and support.
