A collection of Jupyter notebooks, data sets, and other instructional materials for use in a second linear algebra course.
Files corresponding to the presentation at the 2020 Joint MAA/AMS Meetings given by David Austin and Paul E. Fishback, entitled " A Second Linear Algebra Course Emphasizing Data Analysis within a Jupyter Notebook Environment."
You won't need to clone or do anything fancy other than just download files. Later updates will include modules that can be cloned.
Slides for the above presentation.
Tutorial.ipynb: A simple tutorial for performing basic linear algebra operations using Sage in the Jupyter environment.
Activities and Problem Sets.zip: A collection of problem sets and activities, all in PDF format.
Orthogonality_and_Projections and Gram_Schmidt.ipynb: Self-contained activities focusing on orthogonal projections, Gram-Schmidt, etc. Contains a mixture of markup and code so that students can submit the entire notebook in PDF format.
Various notebooks for performing regression: Simple linear, multiple linear, and polynomial.
Data sets for multiple linear regression.
PCA Iris Data.zip and PCA UK Food Data.zip: Each contains a Jupyter notebook and data set for performing PCA.
Various SVD .zip archives: Each contains a data set and Jupyter notebook. Examples include a Netflix recommender system using data obtained from students in this course, image denoising/compression, and decisions rendered by the Supreme Court (See "Singular Value's Subtle Secrets," David James, Michael Lachance, and Joan Remski, College Math Journal vol. 42, 2011, p. 86–95 as well as "A pattern analysis of the second Rehnquist U.S. Supreme Court", Lawrence Sirovich, PNAS, vol 100 , 2003, 7432-7437).
Spectral Clustering.zip: Jupyter notebooks and data for performing spectral clustering. Example data comes from 9/11 terrorist network (62 nodes, unweighted, undirected) and Star Wars co-appearances (21 nodes, weighted, undirected).
A Jupyter notebook focusing on k-means clustering.