|CISC371, Nonlinear Data Analysis: Fall 2023|
Methods for nonlinear data analysis, particularly using numerical optimization. Applications may include: unconstrained data optimization; linear equality constraints; linear inequality constraints; constrained data regression; constrained data classification; evaluating the effectiveness of analysis methods
Lecture and Report Materials:
The navigation panel, on the left, leads to a list of the lectures. These are video recordings and notes for the course. Guides to writing reports, for students and for graders, are also on the left.
A great deal of the instructional material is intended for
asynchronous learning. Regularly scheduled lectures are augmented
with instructional videos and written notes. Each student is
expected to view the videos and read the notes before attending
the scheduled class times.
A guiding principle for this course will be that linear algebra is our main way of describing and implementing nonlinear analysis of structured data. Most other courses use a "calculus-first" principle; instead, we will use a limited amount of calculus to derive the linear algebra that we need for writing code. This "algebra-first" principle will allow us to explore complicated ideas in a concise and scalable manner by using linear algebra as the language of structured data.
Further details can be found from the "Description" link, to the left.
The course contents, including the notes and supplementary material, are provided in the onQ learning management system that acts as a paywall for students who are enrolled in this course. Course content includes: scheduled assessments; non-credit homework; statements of assignment and preparatory material; and other supplementary material for the course.
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