Probabilistic Machine Learning: An Introduction

Probabilistic Machine Learning: An Introduction by Kevin P. Murphy is a comprehensive textbook that introduces machine learning through the unified framework of probabilistic modeling and Bayesian decision theory.

The book covers the mathematical foundations required for machine learning, including probability, statistics, linear algebra, and optimization. It then develops fundamental machine learning methods including supervised learning, regression, classification, neural networks, and deep learning.

It also introduces more advanced topics such as unsupervised learning, transfer learning, graphical models, and neural networks for structured data. The book includes exercises and accompanying Python resources using libraries such as scikit-learn, JAX, PyTorch, and TensorFlow.

Key Topics

• Probabilistic Machine Learning
• Machine Learning
• Artificial Intelligence
• Bayesian Decision Theory
• Probability and Statistics
• Linear Algebra
• Optimization
• Supervised Learning
• Unsupervised Learning
• Linear Regression
• Logistic Regression
• Neural Networks
• Deep Learning
• Classification
• Transfer Learning
• Graphical Models
• Structured Data
• Statistical Learning
• Python for Machine Learning