The DOI matches its Crossref record; 276 of the 276 references it cites were found in a registry or an index.
Mathematics for Machine Learning
Abstract
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
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Cites
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Gradient Projection Memory for Continual Learning
Saha, G., Garg, I., & Roy, K. (2021) · arXiv · doi:10.48550/arxiv.2103.09762 · Cited by 129
Artificial Intelligence for Biology
Hassoun, S., Jefferson, F., Shi, X., Stucky, B., Wang, J., & Rosa, E. (2022) · Integrative and Comparative Biology · doi:10.1093/icb/icab188 · Cited by 64
Machine learning in the analysis of biomolecular simulations
Kaptan, S., & Vattulainen, I. (2022) · Advances in Physics: X · doi:10.1080/23746149.2021.2006080 · Cited by 31
Data Augmented Flatness-aware Gradient Projection for Continual Learning
Yang, E., Shen, L., Wang, Z., Liu, S., Guo, G., & Wang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00518 · Cited by 18
Metadata
- Authors
- Deisenroth, M. P., Faisal, A. A., & Ong, C. S.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2020-02-20
- OpenAlex
- W2974922583
- Cited by
- 573
- References
- 276
- FWCI
- 34.29
- Type
- book
- Language
- en
- Open access
- closed
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