{"slug": "mathematics-of-data-science", "title": "Mathematics of Data Science", "summary": "Thomas Strohmer published a book on the mathematical foundations of data science on arXiv, covering topics from high-dimensional geometry to deep learning. The work provides a comprehensive mathematical framework for core data science methods.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 11 Jul 2026]\n\n# Title:Mathematics of Data Science\n\n[View PDF](/pdf/2607.11938)\n\nAbstract:This book is about the mathematical foundations of data science.\n\n1. Introduction\n\n2. Curses, Blessings, and Surprises in High Dimensions\n\n3. Singular Value Decomposition and Principal Component Analysis\n\n4. Linear Regression and Regularization\n\n5. Graphs, Networks, and Clustering\n\n6. Nonlinear Dimension Reduction and Diffusion Maps\n\n7. Linear Dimension Reduction via Random Projections\n\n8. Optimization for Data Science\n\n9. Classification\n\n10. A Mathematical Introduction to Deep Learning\n\n11. Large Sample Limit of Graph Laplacians\n\n12. Community\n\n13. Concentration of Measure and Gaussian Analysis\n\n14. Matrix Concentration Inequalities\n\n15. Compressive Sensing and Sparsity\n\n16. Low-Rank Matrix Recovery\n\n## Submission history\n\nFrom: Thomas Strohmer [[view email](/show-email/c0ae7ce9/2607.11938)]\n\n**[v1]** Sat, 11 Jul 2026 08:31:44 UTC (15,747 KB)\n\n### Current browse context:\n\ncs.LG\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/mathematics-of-data-science", "canonical_source": "https://arxiv.org/abs/2607.11938", "published_at": "2026-07-16 20:38:48+00:00", "updated_at": "2026-07-16 20:55:03.759492+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["Thomas Strohmer"], "alternates": {"html": "https://wpnews.pro/news/mathematics-of-data-science", "markdown": "https://wpnews.pro/news/mathematics-of-data-science.md", "text": "https://wpnews.pro/news/mathematics-of-data-science.txt", "jsonld": "https://wpnews.pro/news/mathematics-of-data-science.jsonld"}}