Eli Tziperman
APMTH 120 | Spring 2025 | Course Listing
Tuesday & Thursday, 1:30 PM – 2:45 PM
Topics in linear algebra that frequently arise in applications, especially in the analysis of large data sets: linear equations, eigenvalue problems, linear differential equations, principal component analysis, singular value decomposition; data mining and machine learning methods: clustering (unsupervised learning) and classification (supervised) using neural networks and random forests. Examples from physical sciences, biology, climate, commerce, the internet, image processing, and more will be given. The approach is application-motivated, focusing on an intuitive understanding of the algorithms behind these methods obtained by analyzing small data sets. Programming assignments can be done using Python or Matlab.
Recommended Prep: Mathematics 21a,b or equivalent, Computer Science 50 or Applied Mathematics 10 or equivalent programming experience.