कवर छवि

Linear Algebra, Data Science, and Machine Learning

This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algori...

पूर्ण विवरण

में बचाया:
ग्रंथसूची विवरण
मुख्य लेखकों: Calder, Jeff (लेखक), Olver, Peter J. (लेखक)
निगमित लेखक: SpringerLink (Online service)
स्वरूप: इलेक्ट्रोनिक ई-पुस्तक
भाषा:अंग्रेज़ी
प्रकाशित: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
संस्करण:1st ed. 2025.
श्रृंखला:Springer Undergraduate Texts in Mathematics and Technology,
विषय:
Algebras, Linear.
Artificial intelligence > Data processing.
Machine learning.
Mathematical optimization.
Probabilities.
Linear Algebra.
Data Science.
Machine Learning.
Optimization.
Applied Probability.
ऑनलाइन पहुंच:https://doi.org/10.1007/978-3-031-93764-4
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विषय - सूची:
  • Preface
  • 1 Vectors
  • 2 Inner Product, Orthogonality, Norm
  • 3 Matrices
  • 4. How Matrices Interact with Inner Products and Norms
  • 5 Eigenvalues and Singular Values
  • 6 Basics of Optimization
  • 7 Introduction to Machine Learning and Data
  • 8 Principal Component Analysis
  • 9 Graph Theory and Graph-based Learning
  • 10 Neural Networks and Deep Learning
  • 11 Advanced Optimization
  • Bibliography
  • Index.

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