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STS515 Jeremiah D. D. et al.
Table 4. Requisite matching for newer algorithms

Algorithm MATH100 MATH200 STAT100 STAT200

PCA/Kernel PCA x x

Laplace eigenmap x

t-SNE x x
AdaBoost x x
MLP/CNN x x

MCMC x x
Variational
AutoEncoder x x

# Ticks 2 5 2 4

References
1. Dhar, V. (2013). "Data science and prediction". Communications of the
ACM. 56 (12): 64–73. doi:10.1145/2500499.
2. Kolence, K. W. (1973). "The Software Empiricist". ACM SIGMETRICS
Performance Evaluation Review. 2 (2). doi:10.1145/1113644.1113647.
3. WSU (2016). “Data Analytics”, URL https://data-analytics.wsu.edu/197-2/,
retrieved on April 30, 2019.
4. Bischof, G. (2015). Correlation between engineering students’
performance in mathematics and academic success, 122nd ASEE Annual
Conference & Exposition, paper 12476.
5. Ooi, A. (2007) An Analysis of the Teaching of Mathematics in
Undergraduate Engineering Courses, Proceedings of the 2007 AaeE
Conference, Melbourne.
https://conference.eng.unimelb.edu.au/aaee2007/papers/paper-69.pdf

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