Table of contents PRML study Chapter-1 Introduction 1.1 Example: Polynomial Curve Fitting 1.2 Probability Theory 1.3 Model Selection 1.4 The Curse of dimensionality 1.5 Decision Theory 1.6 Information Theory a.1 From Set Theory to Probability Theory Chapter-2 Probability Distributions 2.1 Binary Variables 2.2 Multinomial Variables 2.3 Gaussian Distribution 2.4 The Exponential Family 2.5 Nonparametric Methods Chapter-3 Linear Models 3.1 Linear Basis Function Models 3.2 The Bias-Variance Decomposition 3.3 Bayesian Linear Regression 3.4 Bayesian Model comparison 3.5 The Evidence Approximation 3.6 Limitations of Fixed Basis Functions Chapter-4 Linear Models for Classification 4.1 Discriminant Functions 4.2 Probabilistic Generative Models 4.3 Probabilistic Discriminative Models 4.4 The Laplace Approximation 4.5 Bayesian Logistic Regression Chapter-5 Neural Networks 5.1 Feed-forward network Function 5.2 Network Training 5.3 Error Backpropagation 5.4 The Hessian Matrix 5.5 Regularization in Neural Networks 5.6 Mixture Density Networks 5.7 Bayesian Neural Networks