Classification

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Calculus for Teachers: Complex Integration
By David Bressoud @dbressoud As of 2024, new Launchings columns appear on the third Tuesday of the month. Once we get into complex integration, the theorems become much more technical, so in most cases I will simply state the result, possibly with some indication of why it makes sense that it would be true. For anyone...

Low Stakes, High Rewards: How a Corequisite Course Improved Student Performance in Intro to Proofs
By Ekaterina Yurasovskaya With its highly abstract material and proof-writing that students encounter for the first time, Introduction to Proofs often turns into a gatekeeper to a mathematics major. At the Mathematics Department at Seattle University, we achieved significant results by creating a lecture-free, problem-solving ungraded co-requisite course called Mathematical Communication and Reasoning (MCR). The new...
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            Mathematics of Data Science: A Computational Approach to Clustering and Classification

            This is a textbook on algorithms for clustering and classification in machine learning and their application to mining text and image data.  Algorithms discussed in the book include k-means, k-medoids, linear discriminant analysis (LDA), principal components analysis (PCA), self-organizing maps, nonnegative matrix factorization, tree-based classifiers, and support vector machines.  The most notable omissions are neural...
            Cover of the book titled "Mathematics of Data Science: A Computational Approach to Clustering and Classification" by Daniela Calvetti and Erkki Somersalo. The background is deep red with a grid of four computer-generated images showing abstract, blocky architectural structures. The title is in white text, and the authors' names are listed at the bottom in smaller white font. The top header indicates it belongs to the "Data Science Book Series" by SIAM (Society for Industrial and Applied Mathematics).