contents-pattern-recognition-and-machine-learning

| # | Chapter | Sub-Sections | |---|---------|--------------| | 1 | **Introduction** | 1. Example: Polynomial Curve Fitting 2. Probability Theory 3. Model Selection 4. The Curse of Dimensionality 5. Decision Theory 6. Information Theory | | 2 | **Probability Distributions** | 1. Binary Variables 2. Multinomial Variables 3. The Gaussian Distribution 4. The Exponential Family 5. Non-parametric Methods | | 3 | **Linear Models for Regression** | 1. Linear Basis Function Models 2. The Bias-Variance Decomposition 3. Bayesian Linear Regression 4. **Bayesian Model Comparison** 5. The Evidence Approximation 6. Limitations of Fixed Basis Functions | | 4 | **Linear Models for Classification** | 1. Discriminant Functions 2. Probabilistic Generative Models 3. Probabilistic Discriminative Models 4. The Laplace Approximation 5. Bayesian Logistic Regression | | 5 | **Neural Networks** | 1. Feed-forward Network Functions 2. Network Training 3. Error Back-propagation 4. The Hessian Matrix 5. **Regularization in Neural Networks** 6. Mixture Density Networks 7. **Bayesian Neural Networks** | | 6 | **Kernel Methods** | 1. Dual Representation 2. Constructing Kernels 3. Radial Basis Function Networks 4. Gaussian Processes | | 7 | **Sparse Kernel Machines** | 1. Maximum Margin Classifiers 2. Relevance Vector Machines | | 8 | **Graphical Models** | 1. Bayesian Networks 2. Conditional Independence 3. Markov Random Fields 4. Inference in Graphical Models | | 9 | **Mixture Models and EM** | 1. k-Means Clustering 2. Mixtures of Gaussians 3. An Alternative View of EM 4. The EM Algorithm in General | | 10 | **Approximate Inference** | 1. Variational Inference 2. Illustration: Variational Mixture of Gaussians 3. Variational Linear Regression 4. Exponential Family Distributions 5. Local Variational Methods 6. Variational Logistic Regression 7. Expectation Propagation | | 11 | **Sampling Methods** | 1. Basic Sampling Algorithms 2. Markov Chain Monte Carlo 3. Gibbs Sampling 4. Slice Sampling 5. The Hybrid Monte Carlo Algorithm 6. Estimating the Partition Function | | 12 | **Continuous Latent Variables** | 1. Principal Component Analysis 2. Probabilistic PCA 3. Kernel PCA 4. Non-linear Latent Variable Models | | 13 | **Sequential Data** | 1. Markov Models 2. **Hidden Markov Models** 3. **Linear Dynamical Systems** | | 14 | **Combining Models** | 1. **Bayesian Model Averaging** 2. Committees 3. Boosting 4. Tree-based Models 5. Conditional Mixture Models |