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PRML
  • 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
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PRML study

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  • μ›μ„œ λ‹€μš΄λ‘œλ“œ: link

  • μž‘μ„±μž: μž₯μ§€μˆ˜(simonjisu)

  • λΈ”λ‘œκ·Έ: https://simonjisu.github.io

  • GitBook: https://soo.gitbook.io/prml/

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NextChapter-1 Introduction

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