Read the original document by opening this link in a new tab.
Table of Contents
1 Introduction
2 Probability Distributions
3 Linear Models for Regression
4 Linear Models for Classification
5 Neural Networks
Summary
Pattern Recognition and Machine Learning is a comprehensive textbook that covers the fields of pattern recognition and machine learning. It discusses topics such as polynomial curve fitting, probability theory, linear models for regression and classification, neural networks, and more. The book emphasizes Bayesian methods, graphical models, and kernel-based models, providing a solid foundation for both beginners and advanced researchers in the field.