Mathematics for AI and Machine Learning
A Comprehensive Mathematical Reference for Artificial Intelligence and Machine Learning
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Chapters
- Copyright and Credits
- Chapter 1: Vector Space and Inner Product
- Chapter 2: Matrix
- Chapter 3: Subspaces and Orthogonality
- Chapter 4: QR Decomposition and Numerical Rank
- Chapter 5: Square Matrix and LU Decomposition
- Chapter 6: Geometric Transformation and Eigen Decomposition
- Chapter 7: Symmetric Matrix
- Chapter 8: Singular Value Decomposition (SVD)
- Chapter 9: Matrix Decompositions and Beyond
- Chapter 10: Matrix Block Partitioning
- Chapter 11: Matrix Calculus
- Chapter 12: Optimization Methods
- Chapter 13: Probability and Random Variables
- Chapter 14: Information Theory
- Chapter 15: Bellman Equations and Operators
- Chapter 16: Variational Inference and Latent Variables
- Chapter 17: Score Function and Energy-Based Models
- Chapter 18: Langevin Dynamics and Sampling
- Chapter 19: Stochastic Differential Equations
- Chapter 20: ODE, SDE, and Continuous Limits of Algorithms
- Chapter 21: Fokker-Planck and Distribution Dynamics
- Appendix
