Workshops

Students set up a homogeneous linear system to balance each element in a chemical equation. This workshop demonstrates how understanding the solution space of a linear system can give insight into the feasibility of certain chemical reactions.
Students make use of cofactor expansion in a geometric setting to derive the “Shoelace Formula” for the area of a general polygon given the coordinates of its vertices. Time permitting, students extend this formula to higher dimensions.
MATH 2210
3: Hamming Codes (Sec. 5.4)
Students are introduced to finite field arithmetic. Then, they explore howto use use linear transformations to introduce redundency into a message vector, allowing for error detection and correction. Students get practice encoding and decoding using small Hamming codes.
MATH 2210
4: Best Fit Curves (Sec. 5.6)
Students explore how we can not only use the method of least squares to approximate a linear function to data, but also to determine the best coefficients given any suitable basis functions. They are also introduced to the coefficient of determination, a metric for the quality of the approximation.
This workshop serves as a gentle introduction to the subject of abstract algebra. Students are introduced to the definition of a group and explore some matrix groups. Then, they study group representations, maps from arbitrary groups to matrices which “preserve” the group operation as matrix multiplication.
Students recall the tricky “wrap-around” integration by parts calculations from calculus. By recognizing that the derivative is an invertible linear transformation for some choices of basis functions, we can solve these integrals using matrix multiplication.

Project

MATH 2210
2: Curve Fitting (Sec. 5.6)
This project is a follow-up to Workshop 4. Students gather their own experimental data that models the quantitative relationship between two variables. They fit a linear model to the data, along with a custom-tailored model (chosen by plotting the data). Then, they use the coefficients of determination to analyze the quality of their models.
Sample Project 1 Sample Project 2