Item type | Current location | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|
Course Reserve | 006.31 DEI (Browse shelf) | Not for loan | 034259 |
006.31 COL Introduction to genetic algorithms for scientists and engineers | 006.31 CRI Introduction to support vector machines : and other kernel-based learning methods | 006.31 DAS Deep learning | 006.31 DEI Mathematics for machine learning | 006.31 DEJ Evolutionary computation : a unified approach | 006.31 DRO Science of deep learning | 006.31 FAN Lie group machine learning |
Includes bibliographical references and index.
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.
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