Mathematics For Machine Learning and Inference: A Comprehensive Guide to Regression, Probability, PCA, Bayesian methods, and SVMs

By: Wood, William C.

Master the mathematics behind modern machine learning and inference. Whether you're a student, researcher, or professional, mathematics for machine learning and inference equips you with the essential mathematical foundations to understand, design, and implement powerful algorithms. This comprehensive guide covers linear regression, probabilistic models, Bayesian methods, dimensionality reduction, and support vector machines, blending theory with practical examples to make complex concepts accessible and applicable.

Inside you'll learn probability and statistics such as means, covariances, independence, and essential probability distributions (uniform, bernoulli, binomial, beta, gaussian, gamma, wishart). Vector calculus and optimization: gradients, jacobians, Taylor series, and gradient descent (batch, stochastic, momentum). Parameter estimation: maximum likelihood, MAP estimation, overfitting prevention, and regularization techniques. Bayesian method: model selection, marginal likelihoods, and fully Bayesian treatments. Feature extraction: PCA, LDA, SUD, QR decomposition, kernel methods for high dimensional data. Support vector machines: classification, regression, lagrangian, duality, KKT conditions, and kernel mapping.

With clear explanations and mathematical rigor, this book bridges the gap between goals to analyze data, develop AI systems, or advance in research. Buy your copy today

• Format: Paperback
• Language: English
• ISBN: B0FX9C7WBY
• ISBN13: 9798270905019
• Release Date: October 2025
• Publisher: Independently Published
• Length: 134 Pages
• Weight: 0.72 lbs.
• Dimensions: 0.3" x 8.5" x 11.0"