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CISC371, Nonlinear Optimization: Fall 2025 |
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Calendar Description: 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. Brief Description: When we use machine learning or data analytics methods on structured numerical data, we often want to find an optimal answer. For example, if we want to model the data as being linearly related, we might want to minimize the error between the model and the data. 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. Queen's University is situated on traditional Anishinaabe and Haudenosaunee Territory. |