Classification

Featured Resources

There and Back Again: The Professor I Never Trained to Be
By Lew Ludwig “Alright, now that we understand how to write in the dominant impression, our next essay will use a structure known as “they say/I say” which is like engaging a conversation already in progress.” “Now that we have completed our empathy map and have a better understanding of our stakeholders, we will now start...

Debunking a prominent feature of the “Mediterranean diet” health claim
By Keith Devlin @KeithDevlin@fediscience.org, @profkeithdevlin.bsky.social Last month’s post discussed the subtle (and not so subtle) difficulties that can arise when using statistics to identify causes of phenomena. The example I used that time was the probability that use of the popular pain-reliever Tylenol by a pregnant woman led to an increased risk of her child being...
Filter By
Close Filters
            Showing 1-1 of 1 posts

            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).